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z0o@AP{d&U?cL^xTe(d-2i~8sH);D&PSNWqpL%?hzooVm+vVtUjwVmA<%+_KifM*T> tUj)A&`A0AM?}i5OxdHylY~ly)tKUbPy*%`e!rUkDOHx!$q*llv@PBPBWt9K` diff --git a/dev/_modules/index.html b/dev/_modules/index.html index 9700acb..849ca00 100644 --- a/dev/_modules/index.html +++ b/dev/_modules/index.html @@ -16,7 +16,7 @@ - + @@ -115,7 +115,6 @@

All modules for which code is available

  • neuralop.data.datasets.darcy
  • neuralop.data.transforms.data_processors
    • -
  • neuralop.layers.embeddings
  • neuralop.layers.integral_transform
  • neuralop.layers.neighbor_search
  • neuralop.layers.padding
    • diff --git a/dev/_modules/neuralop/data/datasets/darcy.html b/dev/_modules/neuralop/data/datasets/darcy.html index 7b00334..e9a025b 100644 --- a/dev/_modules/neuralop/data/datasets/darcy.html +++ b/dev/_modules/neuralop/data/datasets/darcy.html @@ -16,7 +16,7 @@ - + diff --git a/dev/_modules/neuralop/data/transforms/data_processors.html b/dev/_modules/neuralop/data/transforms/data_processors.html index e33ab1d..1daa7b6 100644 --- a/dev/_modules/neuralop/data/transforms/data_processors.html +++ b/dev/_modules/neuralop/data/transforms/data_processors.html @@ -16,7 +16,7 @@ - + diff --git a/dev/_modules/neuralop/layers/embeddings.html b/dev/_modules/neuralop/layers/embeddings.html deleted file mode 100644 index 1e69657..0000000 --- a/dev/_modules/neuralop/layers/embeddings.html +++ /dev/null @@ -1,455 +0,0 @@ - - - - - - - neuralop.layers.embeddings — neuraloperator 0.3.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - -
    -
    - - - -
    - - -
    - - - - -
    - -

    Source code for neuralop.layers.embeddings

    -from typing import List
-
-import torch
-from torch import nn
-
-
    
-[docs]
-class GridEmbedding2D(nn.Module):
-    """A simple positional embedding as a regular 2D grid
-    """
-    def __init__(self, grid_boundaries=[[0, 1], [0, 1]]):
-        """GridEmbedding2D applies a simple positional 
-        embedding as a regular 2D grid
-
-        Parameters
-        ----------
-        grid_boundaries : list, optional
-            coordinate boundaries of input grid, by default [[0, 1], [0, 1]]
-        """
-        super().__init__()
-        self.grid_boundaries = grid_boundaries
-        self._grid = None
-        self._res = None
-
-
    
-[docs]
-    def grid(self, spatial_dims, device, dtype):
-        """grid generates 2D grid needed for pos encoding
-        and caches the grid associated with MRU resolution
-
-        Parameters
-        ----------
-        spatial_dims : torch.size
-             sizes of spatial resolution
-        device : literal 'cpu' or 'cuda:*'
-            where to load data
-        dtype : str
-            dtype to encode data
-
-        Returns
-        -------
-        torch.tensor
-            output grids to concatenate 
-        """
-        # handle case of multiple train resolutions
-        if self._grid is None or self._res != spatial_dims: 
-            grid_x, grid_y = regular_grid_2d(spatial_dims,
-                                      grid_boundaries=self.grid_boundaries)
-            grid_x = grid_x.to(device).to(dtype).unsqueeze(0).unsqueeze(0)
-            grid_y = grid_y.to(device).to(dtype).unsqueeze(0).unsqueeze(0)
-            self._grid = grid_x, grid_y
-            self._res = spatial_dims
-
-        return self._grid
    
-
-
-
    
-[docs]
-    def forward(self, data, batched=True):
-        if not batched:
-            if data.ndim == 3:
-                data = data.unsqueeze(0)
-        batch_size = data.shape[0]
-        x, y = self.grid(data.shape[-2:], data.device, data.dtype)
-        out =  torch.cat((data, x.expand(batch_size, -1, -1, -1),
-                          y.expand(batch_size, -1, -1, -1)),
-                         dim=1)
-        # in the unbatched case, the dataloader will stack N 
-        # examples with no batch dim to create one
-        if not batched and batch_size == 1: 
-            return out.squeeze(0)
-        else:
-            return out
    
-
    
-
-
-
    
-[docs]
-class GridEmbeddingND(nn.Module):
-    """A positional embedding as a regular ND grid
-    """
-    def __init__(self, dim: int=2, grid_boundaries=[[0, 1], [0, 1]]):
-        """GridEmbeddingND applies a simple positional 
-        embedding as a regular ND grid
-
-        Parameters
-        ----------
-        dim: int
-            dimensions of positional encoding to apply
-        grid_boundaries : list, optional
-            coordinate boundaries of input grid along each dim, by default [[0, 1], [0, 1]]
-        """
-        super().__init__()
-        self.dim = dim
-        assert self.dim == len(grid_boundaries), f"Error: expected grid_boundaries to be\
-            an iterable of length {self.dim}, received {grid_boundaries}"
-        self.grid_boundaries = grid_boundaries
-        self._grid = None
-        self._res = None
-
-
    
-[docs]
-    def grid(self, spatial_dims: torch.Size, device: str, dtype: torch.dtype):
-        """grid generates ND grid needed for pos encoding
-        and caches the grid associated with MRU resolution
-
-        Parameters
-        ----------
-        spatial_dims : torch.Size
-             sizes of spatial resolution
-        device : literal 'cpu' or 'cuda:*'
-            where to load data
-        dtype : str
-            dtype to encode data
-
-        Returns
-        -------
-        torch.tensor
-            output grids to concatenate 
-        """
-        # handle case of multiple train resolutions
-        if self._grid is None or self._res != spatial_dims: 
-            grids_by_dim = regular_grid_nd(spatial_dims,
-                                      grid_boundaries=self.grid_boundaries)
-            # add batch, channel dims
-            grids_by_dim = [x.to(device).to(dtype).unsqueeze(0).unsqueeze(0) for x in grids_by_dim]
-            self._grid = grids_by_dim
-            self._res = spatial_dims
-
-        return self._grid
    
-
-
-
    
-[docs]
-    def forward(self, data, batched=True):
-        """
-        Params
-        --------
-        data: torch.Tensor
-            assumes shape batch (optional), channels, x_1, x_2, ...x_n
-        batched: bool
-            whether data has a batch dim
-        """
-        # add batch dim if it doesn't exist
-        if not batched:
-            if data.ndim == self.dim + 1:
-                data = data.unsqueeze(0)
-        batch_size = data.shape[0]
-        grids = self.grid(spatial_dims=data.shape[2:],
-                          device=data.device,
-                          dtype=data.dtype)
-        grids = [x.repeat(batch_size, *[1] * (self.dim+1)) for x in grids]
-        out =  torch.cat((data, *grids),
-                         dim=1)
-        return out
    
-
    
-
-    
-
    
-[docs]
-class SinusoidalEmbedding2D(nn.Module):
-    def __init__(self, num_channels, max_positions=10000, endpoint=False):
-        """SinusoidalEmbedding2D applies a 2d sinusoidal positional encoding 
-
-        Parameters
-        ----------
-        num_channels : int
-            number of input channels
-        max_positions : int, optional
-            maximum positions to encode, by default 10000
-        endpoint : bool, optional
-            whether to set endpoint, by default False
-        """
-        super().__init__()
-        self.num_channels = num_channels
-        self.max_positions = max_positions
-        self.endpoint = endpoint
-
-
    
-[docs]
-    def forward(self, x):
-        freqs = torch.arange(
-            start=0, end=self.num_channels // 2, dtype=torch.float32, device=x.device
-        )
-        freqs = freqs / (self.num_channels // 2 - (1 if self.endpoint else 0))
-        freqs = (1 / self.max_positions) ** freqs
-        x = x.ger(freqs.to(x.dtype))
-        x = torch.cat([x.cos(), x.sin()], dim=1)
-        return x
    
-
    
-
-
-
-class RotaryEmbedding2D(nn.Module):
-    def __init__(self, dim, min_freq=1/64, scale=1.):
-        """
-        Applying rotary positional embedding (https://arxiv.org/abs/2104.09864) to the input feature tensor.
-        The crux is the dot product of two rotation matrices R(theta1) and R(theta2) is equal to R(theta2 - theta1).
-        """
-        super().__init__()
-        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.min_freq = min_freq
-        self.scale = scale
-        self.register_buffer('inv_freq', inv_freq, persistent=False)
-
-    def forward(self, coordinates):
-        """coordinates is tensor of [batch_size, num_points]"""
-        coordinates = coordinates * (self.scale / self.min_freq)
-        freqs = torch.einsum('... i , j -> ... i j', coordinates, self.inv_freq)  # [b, n, d//2]
-        return torch.cat((freqs, freqs), dim=-1)  # [b, n, d]
-
-    @staticmethod
-    def apply_1d_rotary_pos_emb(t, freqs):
-        return apply_rotary_pos_emb(t, freqs)
-
-    @staticmethod
-    def apply_2d_rotary_pos_emb(t, freqs_x, freqs_y):
-        """Split the last dimension of features into two equal halves
-           and apply 1d rotary positional embedding to each half."""
-        d = t.shape[-1]
-        t_x, t_y = t[..., :d//2], t[..., d//2:]
-
-        return torch.cat((apply_rotary_pos_emb(t_x, freqs_x),
-                          apply_rotary_pos_emb(t_y, freqs_y)), dim=-1)
-    
-
-# Utility functions for GridEmbedding
-def regular_grid_2d(spatial_dims, grid_boundaries=[[0, 1], [0, 1]]):
-    """
-    Creates a 2 x height x width stack of positional encodings A, where
-    A[:,i,j] = [[x,y]] at coordinate (i,j) on a (height, width) grid. 
-    """
-    height, width = spatial_dims
-
-    xt = torch.linspace(grid_boundaries[0][0], grid_boundaries[0][1],
-                        height + 1)[:-1]
-    yt = torch.linspace(grid_boundaries[1][0], grid_boundaries[1][1],
-                        width + 1)[:-1]
-
-    grid_x, grid_y = torch.meshgrid(xt, yt, indexing='ij')
-
-    grid_x = grid_x.repeat(1, 1)
-    grid_y = grid_y.repeat(1, 1)
-
-    return grid_x, grid_y
-
-def regular_grid_nd(resolutions: List[int], grid_boundaries: List[List[int]]=[[0,1]] * 2):
-    """regular_grid_nd generates a tensor of coordinate points that 
-    describe a bounded regular grid.
-    
-    Creates a dim x res_d1 x ... x res_dn stack of positional encodings A, where
-    A[:,c1,c2,...] = [[d1,d2,...dn]] at coordinate (c1,c2,...cn) on a (res_d1, ...res_dn) grid. 
-
-    Parameters
-    ----------
-    resolutions : List[int]
-        resolution of the output grid along each dimension
-    grid_boundaries : List[List[int]], optional
-        List of pairs [start, end] of the boundaries of the
-        regular grid. Must correspond 1-to-1 with resolutions default [[0,1], [0,1]]
-
-    Returns
-    -------
-    grid: tuple(Tensor)
-    list of tensors describing positional encoding 
-    """
-    assert len(resolutions) == len(grid_boundaries), "Error: inputs must have same number of dimensions"
-    dim = len(resolutions)
-
-    meshgrid_inputs = list()
-    for res, (start,stop) in zip(resolutions, grid_boundaries):
-        meshgrid_inputs.append(torch.linspace(start, stop, res + 1)[:-1])
-    grid = torch.meshgrid(*meshgrid_inputs, indexing='ij')
-    grid = tuple([x.repeat([1]*dim) for x in grid])
-    return grid
-
-  
-# Utility fucntions for Rotary embedding
-# modified from https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-def rotate_half(x):
-    """
-    Split x's channels into two equal halves.
-    """
-    # split the last dimension of x into two equal halves
-    x = x.reshape(*x.shape[:-1], 2, -1)
-    x1, x2 = x.unbind(dim=-2)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(t, freqs):
-    """
-    Apply rotation matrix computed based on freqs to rotate t.
-    t: tensor of shape [batch_size, num_points, dim]
-    freqs: tensor of shape [batch_size, num_points, 1]
-
-    Formula: see equation (34) in https://arxiv.org/pdf/2104.09864.pdf
-    """
-    return (t * freqs.cos()) + (rotate_half(t) * freqs.sin())
-
    - -
    - - - -
    -
    -
    - © Copyright 2024, Jean Kossaifi, David Pitt, Nikola Kovachki, Zongyi Li and Anima Anandkumar.
    -
    -
    -
    - -
    - -
    - - - -
    -
    - - - - - - - - \ No newline at end of file diff --git a/dev/_modules/neuralop/layers/gno_block.html b/dev/_modules/neuralop/layers/gno_block.html deleted file mode 100644 index 5ad397e..0000000 --- a/dev/_modules/neuralop/layers/gno_block.html +++ /dev/null @@ -1,328 +0,0 @@ - - - - - - - neuralop.layers.gno_block — neuraloperator 0.3.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - -
    -
    - - - -
    - - -
    - - - - -
    - -

    Source code for neuralop.layers.gno_block

    -from typing import List
-
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from .channel_mlp import ChannelMLP, LinearChannelMLP
-from .integral_transform import IntegralTransform
-from .neighbor_search import NeighborSearch
-
-
-
    
-[docs]
-class GNOBlock(nn.Module):
-    """GNOBlock implements a Graph Neural Operator layer as described in _[1].
-
-    A GNO layer is a resolution-invariant operator that maps a function defined
-    over one coordinate mesh to another defined over another coordinate mesh using 
-    a pointwise kernel integral that takes contributions from neighbors of distance 1
-    within a graph constructed via neighbor search with a specified radius. 
-
-    The kernel integral computed in IntegralTransform 
-    computes one of the following:
-        (a) \int_{A(x)} k(x, y) dy
-        (b) \int_{A(x)} k(x, y) * f(y) dy
-        (c) \int_{A(x)} k(x, y, f(y)) dy
-        (d) \int_{A(x)} k(x, y, f(y)) * f(y) dy
-    
-    Parameters
-    ----------
-    in_channels : int
-        number of channels in input function. Only used if transform_type
-        is (c) "nonlinear" or (d) "nonlinear_kernelonly"
-    out_channels : int
-        number of channels in output function
-    coord_dim : int
-        dimension of domain on which x and y are defined
-    radius : float
-        radius in which to search for neighbors
-    use_open3d_neighbor_search : _type_, optional
-        _description_, by default None
-    channel_mlp : nn.Module, optional
-        ChannelMLP parametrizing the kernel k. Input dimension
-        should be dim x + dim y or dim x + dim y + dim f.
-        ChannelMLP should not be pointwise and should only operate across
-        channels to preserve the discretization-invariance of the 
-        kernel integral.
-    channel_mlp_layers : List[int], optional
-        list of layer widths to dynamically construct
-        LinearChannelMLP network to parameterize kernel k, by default None
-    channel_mlp_non_linearity : torch.nn function, optional
-        activation function for ChannelMLPLinear above, by default F.gelu
-    transform_type : str, optional
-        Which integral transform to compute. The mapping is:
-        'linear_kernelonly' -> (a)
-        'linear' -> (b) [DEFAULT]
-        'nonlinear_kernelonly' -> (c)
-        'nonlinear' -> (d)
-        If the input f is not given then (a) is computed
-        by default independently of this parameter.
-    use_open3d_neighbor_search: bool, optional
-    use_torch_scatter_reduce : bool, optional
-        whether to reduce in integral computation using a function
-        provided by the extra dependency torch_scatter or the slower
-        native PyTorch implementation, by default True
-
-    References
-    -----------
-    [1]_ Neural Operator: Graph Kernel Network for Partial Differential Equations.
-        Zongyi Li, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, 
-        Anima Anandkumar. ArXiV, 2020 
-    """
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 coord_dim: int,
-                 radius: float,
-                 channel_mlp: nn.Module=None,
-                 channel_mlp_layers: List[int]=None,
-                 channel_mlp_non_linearity=F.gelu,
-                 transform_type="linear",
-                 use_open3d_neighbor_search: bool=True,
-                 use_torch_scatter_reduce=True,):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.coord_dim = coord_dim
-
-        self.radius = radius
-
-        # Create in-to-out nb search module
-        if use_open3d_neighbor_search:
-            assert self.coord_dim == 3, f"Error: open3d is only designed for 3d data, \
-                GNO instantiated for dim={coord_dim}"
-        self.neighbor_search = NeighborSearch(use_open3d=use_open3d_neighbor_search)
-
-        # create proper kernel input channel dim
-        # if nonlinear of either type, add in_features dim
-        # otherwise just add x and y dim
-        kernel_in_dim = self.coord_dim * 2
-        kernel_in_dim_str = "dim(y) + dim(x)"
-        if transform_type == "nonlinear" or transform_type == "nonlinear_kernelonly":
-            kernel_in_dim += self.in_channels
-            kernel_in_dim_str += " + dim(f_y)"
-
-        if channel_mlp is not None:
-            assert channel_mlp.in_channels == kernel_in_dim, f"Error: expected ChannelMLP to take\
-                  input with {kernel_in_dim} channels (feature channels={kernel_in_dim_str}),\
-                      got {channel_mlp.in_channels}."
-            assert channel_mlp.out_channels == out_channels, f"Error: expected ChannelMLP to have\
-                 {out_channels=} but got {channel_mlp.in_channels=}."
-            self.channel_mlp = channel_mlp
-
-        if channel_mlp_layers is not None:
-            if channel_mlp_layers[0] != kernel_in_dim:
-                channel_mlp_layers = [kernel_in_dim] + channel_mlp_layers
-            if channel_mlp_layers[-1] != self.out_channels:
-                channel_mlp_layers.append(self.out_channels)
-            self.channel_mlp = LinearChannelMLP(layers=channel_mlp_layers, non_linearity=channel_mlp_non_linearity)
-
-        # Create integral transform module
-        self.integral_transform = IntegralTransform(
-            channel_mlp=self.channel_mlp,
-            transform_type=transform_type,
-            use_torch_scatter=use_torch_scatter_reduce
-        )
-
-
    
-[docs]
-    def forward(self, y, x, f_y=None):
-        """Compute a GNO neighbor search and kernel integral transform.
-
-        Parameters
-        ----------
-        y : torch.Tensor of shape [n, d1]
-            n points of dimension d1 specifying
-            the space to integrate over.
-            If batched, these must remain constant
-            over the whole batch so no batch dim is needed.
-        x : torch.Tensor of shape [m, d1], default None
-            m points of dimension d1 over which the
-            output function is defined. Must share domain
-            with y
-        f_y : torch.Tensor of shape [batch, n, d2] or [n, d2], default None
-            Function to integrate the kernel against defined
-            on the points y. The kernel is assumed diagonal
-            hence its output shape must be d3 for the transforms
-            (b) or (d). If None, (a) is computed.
-        
-        Output
-        ----------
-        out_features : torch.Tensor of shape [batch, m, d3] or [m, d3]
-            Output function given on the points x.
-            d4 is the output size of the kernel k.
-        """
-        
-        neighbors_dict = self.neighbor_search(data=y, queries=x, radius=self.radius)
-
-        # TODO: compute weights using the neighborhood dict
-        out_features = self.integral_transform(y=y,
-                                               x=x,
-                                               neighbors=neighbors_dict,
-                                               f_y=f_y)
-        
-        return out_features
    
-
    
-
-
-
-
    - -
    - - - -
    -
    -
    - © Copyright 2024, Jean Kossaifi, David Pitt, Nikola Kovachki, Zongyi Li and Anima Anandkumar.
    -
    -
    -
    - -
    - -
    - - - -
    -
    - - - - - - - - \ No newline at end of file diff --git a/dev/_modules/neuralop/layers/integral_transform.html b/dev/_modules/neuralop/layers/integral_transform.html index 1374993..e58d14a 100644 --- a/dev/_modules/neuralop/layers/integral_transform.html +++ b/dev/_modules/neuralop/layers/integral_transform.html @@ -16,7 +16,7 @@ - + diff --git a/dev/_modules/neuralop/layers/neighbor_search.html b/dev/_modules/neuralop/layers/neighbor_search.html index 659cec0..d2d0084 100644 --- a/dev/_modules/neuralop/layers/neighbor_search.html +++ b/dev/_modules/neuralop/layers/neighbor_search.html @@ -16,7 +16,7 @@ - + diff --git a/dev/_modules/neuralop/layers/padding.html b/dev/_modules/neuralop/layers/padding.html index 75b5210..be8c2f4 100644 --- a/dev/_modules/neuralop/layers/padding.html +++ b/dev/_modules/neuralop/layers/padding.html @@ -16,7 +16,7 @@ - + @@ -133,7 +133,7 @@

    Source code for neuralop.layers.padding

             if a list, make sure if matches the dim of (d1, ..., dN)
     padding_mode : {'symmetric', 'one-sided'}, optional
         whether to pad on both sides, by default 'one-sided'
-    resolution_scaling_factor : int ; default is 1
+    output_scaling_factor : int ; default is 1
 
     Notes
     -----
@@ -145,14 +145,14 @@ 
    Source code for neuralop.layers.padding
             self,
         domain_padding,
         padding_mode="one-sided",
-        resolution_scaling_factor: Union[int, List[int]] = 1,
+        output_scaling_factor: Union[int, List[int]] = 1,
     ):
         super().__init__()
         self.domain_padding = domain_padding
         self.padding_mode = padding_mode.lower()
-        if resolution_scaling_factor is None:
-            resolution_scaling_factor = 1
-        self.resolution_scaling_factor: Union[int, List[int]] = resolution_scaling_factor
+        if output_scaling_factor is None:
+            output_scaling_factor = 1
+        self.output_scaling_factor: Union[int, List[int]] = output_scaling_factor
 
         # dict(f'{resolution}'=padding) such that padded = F.pad(x, indices)
         self._padding = dict()
@@ -185,12 +185,12 @@ 
    Source code for neuralop.layers.padding
                 "(excluding batch, ch)"
         )
 
-        resolution_scaling_factor = self.resolution_scaling_factor
-        if not isinstance(self.resolution_scaling_factor, list):
+        output_scaling_factor = self.output_scaling_factor
+        if not isinstance(self.output_scaling_factor, list):
             # if unset by the user, scaling_factor will be 1 be default,
-            # so `resolution_scaling_factor` should never be None.
-            resolution_scaling_factor: List[float] = validate_scaling_factor(
-                self.resolution_scaling_factor, len(resolution), n_layers=None
+            # so `output_scaling_factor` should never be None.
+            output_scaling_factor: List[float] = validate_scaling_factor(
+                self.output_scaling_factor, len(resolution), n_layers=None
             )
 
         try:
@@ -209,7 +209,7 @@ 
    Source code for neuralop.layers.padding
                 output_pad = padding
 
             output_pad = [
-                round(i * j) for (i, j) in zip(resolution_scaling_factor, output_pad)
+                round(i * j) for (i, j) in zip(output_scaling_factor, output_pad)
             ]
 
             # padding is being applied in reverse order
@@ -260,7 +260,7 @@ 
    Source code for neuralop.layers.padding
                 output_shape = padded.shape[2:]
 
             output_shape = [
-                round(i * j) for (i, j) in zip(resolution_scaling_factor, output_shape)
+                round(i * j) for (i, j) in zip(output_scaling_factor, output_shape)
             ]
 
             self._unpad_indices[f"{[i for i in output_shape]}"] = unpad_indices
diff --git a/dev/_modules/neuralop/layers/skip_connections.html b/dev/_modules/neuralop/layers/skip_connections.html
index 5fd8131..7ddff92 100644
--- a/dev/_modules/neuralop/layers/skip_connections.html
+++ b/dev/_modules/neuralop/layers/skip_connections.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
@@ -190,7 +190,7 @@ 
    Source code for neuralop.layers.skip_connections
    
         super().__init__()
         if out_features is not None and in_features != out_features:
             raise ValueError(
-                f"Got in_features={in_features} and out_features={out_features}, "
+                f"Got in_features={in_features} and out_features={out_features}"
                 "but these two must be the same for soft-gating"
             )
         self.in_features = in_features
diff --git a/dev/_modules/neuralop/layers/spectral_convolution.html b/dev/_modules/neuralop/layers/spectral_convolution.html
index d19152f..275644a 100644
--- a/dev/_modules/neuralop/layers/spectral_convolution.html
+++ b/dev/_modules/neuralop/layers/spectral_convolution.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
@@ -333,11 +333,16 @@ 
    Source code for neuralop.layers.spectral_convolution
            tensor weight individually
     init_std : float or 'auto', default is 'auto'
         std to use for the init
+    n_layers : int, optional
+        Number of Fourier Layers, by default 4
     factorization : str or None, {'tucker', 'cp', 'tt'}, default is None
         If None, a single dense weight is learned for the FNO.
         Otherwise, that weight, used for the contraction in the Fourier domain
         is learned in factorized form. In that case, `factorization` is the
         tensor factorization of the parameters weight used.
+    joint_factorization : bool, optional
+        Whether all the Fourier Layers should be parametrized by a single tensor
+        (vs one per layer), by default False Ignored if ``factorization is None``
     rank : float or rank, optional
         Rank of the tensor factorization of the Fourier weights, by default 1.0
         Ignored if ``factorization is None``
@@ -356,7 +361,7 @@ 
    Source code for neuralop.layers.spectral_convolution
        decomposition_kwargs : dict, optional, default is {}
         Optionaly additional parameters to pass to the tensor decomposition
         Ignored if ``factorization is None``
-    complex: bool, optional
+    complex_data: bool, optional
         whether data takes on complex values in the spatial domain, by default False
         if True, uses different logic for FFT contraction and uses full FFT instead of real-valued
     """
@@ -366,29 +371,30 @@ 
    Source code for neuralop.layers.spectral_convolution
    in_channels,
         out_channels,
         n_modes,
-        complex=False,
         max_n_modes=None,
         bias=True,
+        n_layers=1,
         separable=False,
-        resolution_scaling_factor: Optional[Union[Number, List[Number]]] = None,
+        output_scaling_factor: Optional[Union[Number, List[Number]]] = None,
         fno_block_precision="full",
         rank=0.5,
         factorization=None,
         implementation="reconstructed",
         fixed_rank_modes=False,
+        joint_factorization=False,
         decomposition_kwargs: Optional[dict] = None,
-        dtype=torch.float32,
+        complex_data: bool=False,
         init_std="auto",
         fft_norm="forward",
         device=None,
+        dtype=None,
     ):
         super().__init__(dtype=dtype, device=device)
 
         self.in_channels = in_channels
         self.out_channels = out_channels
-
-        self.dtype = dtype
-        self.complex = complex
+        self.joint_factorization = joint_factorization
+        self.complex_data = complex_data
 
         # n_modes is the total number of modes kept along each dimension
         self.n_modes = n_modes
@@ -403,11 +409,12 @@ 
    Source code for neuralop.layers.spectral_convolution
    self.fno_block_precision = fno_block_precision
         self.rank = rank
         self.factorization = factorization
+        self.n_layers = n_layers
         self.implementation = implementation
 
-        self.resolution_scaling_factor: Union[
+        self.output_scaling_factor: Union[
             None, List[List[float]]
-        ] = validate_scaling_factor(resolution_scaling_factor, self.order)
+        ] = validate_scaling_factor(output_scaling_factor, self.order, n_layers)
 
         if init_std == "auto":
             init_std = (2 / (in_channels + out_channels))**0.5
@@ -438,36 +445,50 @@ 
    Source code for neuralop.layers.spectral_convolution
    self.separable = separable
 
         tensor_kwargs = decomposition_kwargs if decomposition_kwargs is not None else {}
-
-        # Create/init spectral weight tensor
-
         if factorization is None:
-            self.weight = torch.tensor(weight_shape, dtype=torch.cfloat)
+            self.weight = nn.ModuleList(
+                [torch.tensor(weight_shape, dtype=torch.cfloat) for _ in range(n_layers)]
+            )
+            for w in self.weight:
+                w.normal_(0, init_std)
+        elif joint_factorization:
+            self.weight = FactorizedTensor.new(
+                (n_layers, *weight_shape),
+                rank=self.rank, factorization=factorization, fixed_rank_modes=fixed_rank_modes,
+                dtype=torch.cfloat, **tensor_kwargs,
+            )
+            self.weight.normal_(0, init_std)
         else:
-            self.weight = FactorizedTensor.new(weight_shape, rank=self.rank, 
+            self.weight = nn.ModuleList([
+                FactorizedTensor.new(weight_shape, rank=self.rank, 
                                      factorization=factorization, fixed_rank_modes=fixed_rank_modes,
                                      **tensor_kwargs, dtype=torch.cfloat) 
-        self.weight.normal_(0, init_std)
-        
+                for _ in range(n_layers)]
+            )
+            for w in self.weight:
+                w.normal_(0, init_std)
         self._contract = get_contract_fun(
-            self.weight, implementation=implementation, separable=separable
+            self.weight[0], implementation=implementation, separable=separable
         )
 
         if bias:
             self.bias = nn.Parameter(
-                init_std * torch.randn(*(tuple([self.out_channels]) + (1,) * self.order))
+                init_std * torch.randn(*((n_layers, self.out_channels) + (1,) * self.order))
             )
         else:
             self.bias = None
 
+    def _get_weight(self, index):
+        return self.weight[index]
+
 
    
 [docs]
-    def transform(self, x, output_shape=None):
+    def transform(self, x, layer_index=0, output_shape=None):
         in_shape = list(x.shape[2:])
 
-        if self.resolution_scaling_factor is not None and output_shape is None:
+        if self.output_scaling_factor is not None and output_shape is None:
             out_shape = tuple(
-                [round(s * r) for (s, r) in zip(in_shape, self.resolution_scaling_factor)]
+                [round(s * r) for (s, r) in zip(in_shape, self.output_scaling_factor[layer_index])]
             )
         elif output_shape is not None:
             out_shape = output_shape
@@ -493,14 +514,14 @@ 
    Source code for neuralop.layers.spectral_convolution
    # the real FFT is skew-symmetric, so the last mode has a redundacy if our data is real in space 
         # As a design choice we do the operation here to avoid users dealing with the +1
         # if we use the full FFT we cannot cut off informtion from the last mode
-        if not self.complex:
+        if not self.complex_data:
             n_modes[-1] = n_modes[-1] // 2 + 1
         self._n_modes = n_modes
 
 
    
 [docs]
     def forward(
-        self, x: torch.Tensor, output_shape: Optional[Tuple[int]] = None
+        self, x: torch.Tensor, indices=0, output_shape: Optional[Tuple[int]] = None
     ):
         """Generic forward pass for the Factorized Spectral Conv
 
@@ -508,6 +529,8 @@ 
    Source code for neuralop.layers.spectral_convolution
            ----------
         x : torch.Tensor
             input activation of size (batch_size, channels, d1, ..., dN)
+        indices : int, default is 0
+            if joint_factorization, index of the layers for n_layers > 1
 
         Returns
         -------
@@ -516,14 +539,14 @@ 
    Source code for neuralop.layers.spectral_convolution
    batchsize, channels, *mode_sizes = x.shape
 
         fft_size = list(mode_sizes)
-        if not self.complex:
+        if not self.complex_data:
             fft_size[-1] = fft_size[-1] // 2 + 1  # Redundant last coefficient in real spatial data
         fft_dims = list(range(-self.order, 0))
 
         if self.fno_block_precision == "half":
             x = x.half()
 
-        if self.complex:
+        if self.complex_data:
             x = torch.fft.fftn(x, norm=self.fft_norm, dim=fft_dims)
         else: 
             x = torch.fft.rfftn(x, norm=self.fft_norm, dim=fft_dims)
@@ -551,14 +574,14 @@ 
    Source code for neuralop.layers.spectral_convolution
    slices_w = [slice(None)] # channels
         else:
             slices_w =  [slice(None), slice(None)] # in_channels, out_channels
-        if self.complex:
+        if self.complex_data:
             slices_w += [slice(start//2, -start//2) if start else slice(start, None) for start in starts]
         else:
             # The last mode already has redundant half removed in real FFT
             slices_w += [slice(start//2, -start//2) if start else slice(start, None) for start in starts[:-1]]
             slices_w += [slice(None, -starts[-1]) if starts[-1] else slice(None)]
         
-        weight = self.weight[slices_w]
+        weight = self._get_weight(indices)[slices_w]
 
         # if separable conv, weight tensor only has one channel dim
         if self.separable:
@@ -569,15 +592,15 @@ 
    Source code for neuralop.layers.spectral_convolution
    starts = [(size - min(size, n_mode)) for (size, n_mode) in zip(list(x.shape[2:]), list(weight.shape[weight_start_idx:]))]
         slices_x =  [slice(None), slice(None)] # Batch_size, channels
 
-        if self.complex:
+        if self.complex_data:
             slices_x += [slice(start//2, -start//2) if start else slice(start, None) for start in starts]
         else:
             slices_x += [slice(start//2, -start//2) if start else slice(start, None) for start in starts[:-1]]
             slices_x += [slice(None, -starts[-1]) if starts[-1] else slice(None)] # The last mode already has redundant half removed
         out_fft[slices_x] = self._contract(x[slices_x], weight, separable=self.separable)
 
-        if self.resolution_scaling_factor is not None and output_shape is None:
-            mode_sizes = tuple([round(s * r) for (s, r) in zip(mode_sizes, self.resolution_scaling_factor)])
+        if self.output_scaling_factor is not None and output_shape is None:
+            mode_sizes = tuple([round(s * r) for (s, r) in zip(mode_sizes, self.output_scaling_factor[indices])])
 
         if output_shape is not None:
             mode_sizes = output_shape
@@ -585,16 +608,32 @@ 
    Source code for neuralop.layers.spectral_convolution
    if self.order > 1:
             out_fft = torch.fft.fftshift(out_fft, dim=fft_dims[:-1])
         
-        if self.complex:
+        if self.complex_data:
             x = torch.fft.ifftn(out_fft, s=mode_sizes, dim=fft_dims, norm=self.fft_norm)
         else:
             x = torch.fft.irfftn(out_fft, s=mode_sizes, dim=fft_dims, norm=self.fft_norm)
 
         if self.bias is not None:
-            x = x + self.bias
+            x = x + self.bias[indices, ...]
 
         return x
    
-
    
+
+
+
    
+[docs]
+    def get_conv(self, indices):
+        """Returns a sub-convolutional layer from the joint parametrize main-convolution
+
+        The parametrization of sub-convolutional layers is shared with the main one.
+        """
+        if self.n_layers == 1:
+            Warning("A single convolution is parametrized, directly use the main class.")
+
+        return SubConv(self, indices)
    
+
+
+    def __getitem__(self, indices):
+        return self.get_conv(indices)
    
 
 
 
@@ -651,16 +690,16 @@ 
    Source code for neuralop.layers.spectral_convolution
    slice(None, self.n_modes[0]), # :half_n_modes[0]]
         )
         out_fft[slices] = self._contract(
-            x[slices], self.weight[slices], separable=self.separable
+            x[slices], self._get_weight(indices)[slices], separable=self.separable
         )
 
-        if self.resolution_scaling_factor is not None:
-            width = round(width * self.resolution_scaling_factor[0])
+        if self.output_scaling_factor is not None:
+            width = round(width * self.output_scaling_factor[0])
 
         x = torch.fft.irfft(out_fft, n=width, norm=self.fft_norm)
 
         if self.bias is not None:
-            x = x + self.bias[...]
+            x = x + self.bias[indices, ...]
 
         return x
    
 
    
@@ -678,7 +717,7 @@ 
    Source code for neuralop.layers.spectral_convolution
    
 [docs]
-    def forward(self, x):
+    def forward(self, x, indices=0):
         batchsize, channels, height, width = x.shape
 
         x = torch.fft.rfft2(x.float(), norm=self.fft_norm, dim=(-2, -1))
@@ -703,28 +742,28 @@ 
    Source code for neuralop.layers.spectral_convolution
    slice(-self.n_modes[0] // 2, None),  # -half_n_modes[0]:,
             slice(self.n_modes[1]),  # ......      :half_n_modes[1]]
         )
-        print(f'2D: {x[slices0].shape=}, {self.weight[slices0].shape=}, {self.weight.shape=}')
+        print(f'2D: {x[slices0].shape=}, {self._get_weight(indices)[slices0].shape=}, {self._get_weight(indices).shape=}')
 
         """Upper block (truncate high frequencies)."""
         out_fft[slices0] = self._contract(
-            x[slices0], self.weight[slices1], separable=self.separable
+            x[slices0], self._get_weight(indices)[slices1], separable=self.separable
         )
 
         """Lower block"""
         out_fft[slices1] = self._contract(
-            x[slices1], self.weight[slices0], separable=self.separable
+            x[slices1], self._get_weight(indices)[slices0], separable=self.separable
         )
 
-        if self.resolution_scaling_factor is not None:
-            width = round(width * self.resolution_scaling_factor[0])
-            height = round(height * self.resolution_scaling_factor[1])
+        if self.output_scaling_factor is not None:
+            width = round(width * self.output_scaling_factor[indices][0])
+            height = round(height * self.output_scaling_factor[indices][1])
 
         x = torch.fft.irfft2(
             out_fft, s=(height, width), dim=(-2, -1), norm=self.fft_norm
         )
 
         if self.bias is not None:
-            x = x + self.bias
+            x = x + self.bias[indices, ...]
 
         return x
    
 
    
@@ -742,7 +781,7 @@ 
    Source code for neuralop.layers.spectral_convolution
    
 [docs]
-    def forward(self, x):
+    def forward(self, x, indices=0):
         batchsize, channels, height, width, depth = x.shape
 
         x = torch.fft.rfftn(x.float(), norm=self.fft_norm, dim=[-3, -2, -1])
@@ -784,34 +823,34 @@ 
    Source code for neuralop.layers.spectral_convolution
            """Upper block -- truncate high frequencies."""
         out_fft[slices0] = self._contract(
-            x[slices0], self.weight[slices3], separable=self.separable
+            x[slices0], self._get_weight(indices)[slices3], separable=self.separable
         )
 
         """Low-pass filter for indices 2 & 4, and high-pass filter for index 3."""
         out_fft[slices1] = self._contract(
-            x[slices1], self.weight[slices2], separable=self.separable
+            x[slices1], self._get_weight(indices)[slices2], separable=self.separable
         )
 
         """Low-pass filter for indices 3 & 4, and high-pass filter for index 2."""
         out_fft[slices2] = self._contract(
-            x[slices2], self.weight[slices1], separable=self.separable
+            x[slices2], self._get_weight(indices)[slices1], separable=self.separable
         )
 
         """Lower block -- low-cut filter in indices 2 & 3
         and high-cut filter in index 4."""
         out_fft[slices3] = self._contract(
-            x[slices3], self.weight[slices0], separable=self.separable
+            x[slices3], self._get_weight(indices)[slices0], separable=self.separable
         )
 
-        if self.resolution_scaling_factor is not None:
-            width = round(width * self.resolution_scaling_factor[0])
-            height = round(height * self.resolution_scaling_factor[1])
-            depth = round(depth * self.resolution_scaling_factor[2])
+        if self.output_scaling_factor is not None:
+            width = round(width * self.output_scaling_factor[0])
+            height = round(height * self.output_scaling_factor[1])
+            depth = round(depth * self.output_scaling_factor[2])
 
         x = torch.fft.irfftn(out_fft, s=(height, width, depth), dim=[-3, -2, -1], norm=self.fft_norm)
 
         if self.bias is not None:
-            x = x + self.bias
+            x = x + self.bias[indices, ...]
         return x
    
 
    
 
diff --git a/dev/_modules/neuralop/layers/spherical_convolution.html b/dev/_modules/neuralop/layers/spherical_convolution.html
index b039d8d..f1261f6 100644
--- a/dev/_modules/neuralop/layers/spherical_convolution.html
+++ b/dev/_modules/neuralop/layers/spherical_convolution.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
@@ -426,9 +426,10 @@ 
    Source code for neuralop.layers.spherical_convolution
    n_modes,
         max_n_modes=None,
         bias=True,
+        n_layers=1,
         separable=False,
-        resolution_scaling_factor: Optional[Union[Number, List[Number]]] = None,
-        fno_block_precision="full",
+        output_scaling_factor: Optional[Union[Number, List[Number]]] = None,
+        # fno_block_precision="full",
         rank=0.5,
         factorization="cp",
         implementation="reconstructed",
@@ -440,7 +441,6 @@ 
    Source code for neuralop.layers.spherical_convolution
    sht_grids="equiangular",
         device=None,
         dtype=torch.float32,
-        complex=False # dummy param until we unify dtype interface
     ):
         super().__init__(dtype=dtype, device=device)
 
@@ -461,11 +461,12 @@ 
    Source code for neuralop.layers.spherical_convolution
    self.rank = rank
         self.factorization = factorization
+        self.n_layers = n_layers
         self.implementation = implementation
 
-        self.resolution_scaling_factor: Union[
+        self.output_scaling_factor: Union[
             None, List[List[float]]
-        ] = validate_scaling_factor(resolution_scaling_factor, self.order)
+        ] = validate_scaling_factor(output_scaling_factor, self.order, n_layers)
 
         if init_std == "auto":
             init_std = (2 / (in_channels + out_channels))**0.5
@@ -496,64 +497,84 @@ 
    Source code for neuralop.layers.spherical_convolution
    else:
             weight_shape = (in_channels, out_channels, *self.n_modes[:-1])
         self.separable = separable
-        self.weight = FactorizedTensor.new(
-                    weight_shape,
-                    rank=self.rank,
-                    factorization=factorization,
-                    fixed_rank_modes=fixed_rank_modes,
-                    **decomposition_kwargs,
-                )
-        self.weight.normal_(0, init_std)
 
+        if joint_factorization:
+            self.weight = FactorizedTensor.new(
+                (self.n_layers, *weight_shape),
+                rank=self.rank,
+                factorization=factorization,
+                fixed_rank_modes=fixed_rank_modes,
+                **decomposition_kwargs,
+            )
+            self.weight.normal_(0, init_std)
+        else:
+            self.weight = nn.ModuleList(
+                [
+                    FactorizedTensor.new(
+                        weight_shape,
+                        rank=self.rank,
+                        factorization=factorization,
+                        fixed_rank_modes=fixed_rank_modes,
+                        **decomposition_kwargs,
+                    )
+                    for _ in range(n_layers)
+                ]
+            )
+            for w in self.weight:
+                w.normal_(0, init_std)
         self._contract = get_contract_fun(
-            self.weight, implementation=implementation, separable=separable
+            self.weight[0], implementation=implementation, separable=separable
         )
 
         if bias:
             self.bias = nn.Parameter(
                 init_std
-                * torch.randn(*(tuple([self.out_channels]) + (1,) * self.order))
+                * torch.randn(*((n_layers, self.out_channels) + (1,) * self.order))
             )
         else:
             self.bias = None
 
         self.sht_norm = sht_norm
         if isinstance(sht_grids, str):
-            sht_grids = [sht_grids]*2
+            sht_grids = [sht_grids]*(self.n_layers + 1)
         self.sht_grids = sht_grids
-        print(f"{self.sht_grids=}")
         self.sht_handle = SHT(dtype=self.dtype, device=self.device)
+
+    def _get_weight(self, index):
+        return self.weight[index]
     
 
    
 [docs]
-    def transform(self, x, output_shape=None):
+    def transform(self, x, layer_index=0, output_shape=None):
         *_, in_height, in_width = x.shape
 
-        if self.resolution_scaling_factor is not None and output_shape is None:
-            height = round(in_height * self.resolution_scaling_factor[0])
-            width = round(in_width * self.resolution_scaling_factor[1])
+        if self.output_scaling_factor is not None and output_shape is None:
+            height = round(in_height * self.output_scaling_factor[layer_index][0])
+            width = round(in_width * self.output_scaling_factor[layer_index][1])
         elif output_shape is not None:
             height, width = output_shape[0], output_shape[1]
         else:
             height, width = in_height, in_width
 
         # Return the identity if the resolution and grid of the input and output are the same
-        if ((in_height, in_width) == (height, width)) and (self.sht_grids[0] == self.sht_grids[1]):
+        if ((in_height, in_width) == (height, width)) and (self.sht_grids[layer_index] == self.sht_grids[layer_index+1]):
             return x
         else:
-            coefs = self.sht_handle.sht(x, s=self.n_modes, norm=self.sht_norm, grid=self.sht_grids[0])
-            return self.sht_handle.isht(coefs, s=(height, width), norm=self.sht_norm, grid=self.sht_grids[1])
    
+            coefs = self.sht_handle.sht(x, s=self.n_modes, norm=self.sht_norm, grid=self.sht_grids[layer_index])
+            return self.sht_handle.isht(coefs, s=(height, width), norm=self.sht_norm, grid=self.sht_grids[layer_index + 1])
    
 
 
 
    
 [docs]
-    def forward(self, x, output_shape=None):
+    def forward(self, x, indices=0, output_shape=None):
         """Generic forward pass for the Factorized Spectral Conv
 
         Parameters
         ----------
         x : torch.Tensor
             input activation of size (batch_size, channels, d1, ..., dN)
+        indices : int, default is 0
+            if joint_factorization, index of the layers for n_layers > 1
 
         Returns
         -------
@@ -561,28 +582,28 @@ 
    Source code for neuralop.layers.spherical_convolution
            """
         batchsize, channels, height, width = x.shape
 
-        if self.resolution_scaling_factor is not None and output_shape is None:
-            scaling_factors = self.resolution_scaling_factor
+        if self.output_scaling_factor is not None and output_shape is None:
+            scaling_factors = self.output_scaling_factor[indices]
             height = round(height * scaling_factors[0])
             width = round(width * scaling_factors[1])
         elif output_shape is not None:
             height, width = output_shape[0], output_shape[1]
 
         out_fft = self.sht_handle.sht(x, s=(self.n_modes[0], self.n_modes[1]//2),
-                                      norm=self.sht_norm, grid=self.sht_grids[0])
+                                      norm=self.sht_norm, grid=self.sht_grids[indices])
 
         out_fft = self._contract(
             out_fft[:, :, :self.n_modes[0], :self.n_modes[1]//2],
-            self.weight[:, :, :self.n_modes[0]],
+            self._get_weight(indices)[:, :, :self.n_modes[0]],
             separable=self.separable,
             dhconv=True,
         )
 
         x = self.sht_handle.isht(out_fft, s=(height, width), norm=self.sht_norm,
-                                 grid=self.sht_grids[1])
+                                 grid=self.sht_grids[indices+1])
 
         if self.bias is not None:
-            x = x + self.bias
+            x = x + self.bias[indices, ...]
 
         return x
    
 
@@ -597,7 +618,25 @@ 
    Source code for neuralop.layers.spherical_convolution
    n_modes = [n_modes]
         else:
             n_modes = list(n_modes)
-        self._n_modes = n_modes
    
+        self._n_modes = n_modes
+
+
    
+[docs]
+    def get_conv(self, indices):
+        """Returns a sub-convolutional layer from the joint parametrize main-convolution
+
+        The parametrization of sub-convolutional layers is shared with the main one.
+        """
+        if self.n_layers == 1:
+            raise ValueError(
+                "A single convolution is parametrized, directly use the main class."
+            )
+
+        return SubConv(self, indices)
    
+
+
+    def __getitem__(self, indices):
+        return self.get_conv(indices)
    
 
 
    
 
diff --git a/dev/_modules/neuralop/losses/data_losses.html b/dev/_modules/neuralop/losses/data_losses.html
index 46444bd..8b22e36 100644
--- a/dev/_modules/neuralop/losses/data_losses.html
+++ b/dev/_modules/neuralop/losses/data_losses.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
diff --git a/dev/_modules/neuralop/models/base_model.html b/dev/_modules/neuralop/models/base_model.html
index e52e4fa..8ce89b8 100644
--- a/dev/_modules/neuralop/models/base_model.html
+++ b/dev/_modules/neuralop/models/base_model.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
diff --git a/dev/_modules/neuralop/models/fno.html b/dev/_modules/neuralop/models/fno.html
index 116c6c7..3d2cb8e 100644
--- a/dev/_modules/neuralop/models/fno.html
+++ b/dev/_modules/neuralop/models/fno.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
@@ -114,11 +114,7 @@
         
   
    Source code for neuralop.models.fno
     from functools import partialmethod
-from typing import Tuple, List, Union
 
-Number = Union[float, int]
-
-import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
@@ -133,193 +129,145 @@ 
    Source code for neuralop.models.fno
     
    
 [docs]
 class FNO(BaseModel, name='FNO'):
-    """N-Dimensional Fourier Neural Operator. The FNO learns a mapping between
-    spaces of functions discretized over regular grids. 
-    
-    The key component of an FNO is its SpectralConv layer (see ``neuralop.layers.spectral_convolution``), which
-    is similar to a standard CNN conv layer but operates in the frequency domain.
-
-    For more information, refer to :ref:`fno-guide`.
+    """N-Dimensional Fourier Neural Operator
 
     Parameters
     ----------
-    n_modes : Tuple[int]
+    n_modes : int tuple
         number of modes to keep in Fourier Layer, along each dimension
         The dimensionality of the FNO is inferred from ``len(n_modes)``
-    in_channels : int
-        Number of channels in input function
-    out_channels : int
-        Number of channels in output function
     hidden_channels : int
-        width of the FNO (i.e. number of channels), by default 256
+        width of the FNO (i.e. number of channels)
+    in_channels : int, optional
+        Number of input channels, by default 3
+    out_channels : int, optional
+        Number of output channels, by default 1
+    lifting_channels : int, optional
+        number of hidden channels of the lifting block of the FNO, by default 256
+    projection_channels : int, optional
+        number of hidden channels of the projection block of the FNO, by default 256
     n_layers : int, optional
         Number of Fourier Layers, by default 4
-
-    Documentation for more advanced parameters is below.
-
-    Examples
-    ---------
-    
-    >>> from neuralop.models import FNO
-    >>> model = FNO(n_modes=(12,12), in_channels=1, out_channels=1, hidden_channels=64)
-    >>> model
-    FNO(
-    (positional_embedding): GridEmbeddingND()
-    (fno_blocks): FNOBlocks(
-        (convs): SpectralConv(
-        (weight): ModuleList(
-            (0-3): 4 x DenseTensor(shape=torch.Size([64, 64, 12, 7]), rank=None)
-        )
-        )
-            ... torch.nn.Module printout truncated ...
-
-
-    Other parameters
-    ------------------
-    lifting_channel_ratio : int, optional
-        ratio of lifting channels to hidden_channels, by default 2
-        The number of liting channels in the lifting block of the FNO is
-        lifting_channel_ratio * hidden_channels (e.g. default 512)
-    projection_channel_ratio : int, optional
-        ratio of projection channels to hidden_channels, by default 2
-        The number of projection channels in the projection block of the FNO is
-        projection_channel_ratio * hidden_channels (e.g. default 512)
-    positional_embedding : Union[str, nn.Module], optional
-        Positional embedding to apply to last channels of raw input
-        before being passed through the FNO. Defaults to "grid"
-
-        * If "grid", appends a grid positional embedding with default settings to 
+    positional_embedding : str literal | GridEmbedding2D | GridEmbeddingND | None
+        if "grid", appends a grid positional embedding with default settings to 
         the last channels of raw input. Assumes the inputs are discretized
         over a grid with entry [0,0,...] at the origin and side lengths of 1.
+        If an initialized GridEmbedding, uses this module directly
+        See `neuralop.embeddings.GridEmbeddingND` for details
+        if None, does nothing
+    max_n_modes : None or int tuple, default is None
+        * If not None, this allows to incrementally increase the number of
+          modes in Fourier domain during training. Has to verify n <= N
+          for (n, m) in zip(max_n_modes, n_modes).
 
-        * If an initialized GridEmbedding module, uses this module directly
-        See :mod:`neuralop.embeddings.GridEmbeddingND` for details.
-
-        * If None, does nothing
+        * If None, all the n_modes are used.
 
+        This can be updated dynamically during training.
+    fno_block_precision : str {'full', 'half', 'mixed'}
+        if 'full', the FNO Block runs in full precision
+        if 'half', the FFT, contraction, and inverse FFT run in half precision
+        if 'mixed', the contraction and inverse FFT run in half precision
+    stabilizer : str {'tanh'} or None, optional
+        By default None, otherwise tanh is used before FFT in the FNO block
+    use_channel_mlp : bool, optional
+        Whether to use a ChannelMLP layer after each FNO block, by default False
+    channel_mlp_dropout : float , optional
+        droupout parameter of ChannelMLP layer, by default 0
+    channel_mlp_expansion : float, optional
+        expansion parameter of ChannelMLP layer, by default 0.5
     non_linearity : nn.Module, optional
-        Non-Linear activation function module to use, by default F.gelu
-    norm : str {"ada_in", "group_norm", "instance_norm"}, optional
+        Non-Linearity module to use, by default F.gelu
+    norm : Literal["ada_in", "group_norm", "instance_norm"], optional
         Normalization layer to use, by default None
-    complex : bool, optional
-        Whether data is complex-valued (default False)
-        if True, initializes complex-valued modules.
-    channel_mlp_dropout : float, optional
-        dropout parameter for ChannelMLP in FNO Block, by default 0
-    channel_mlp_expansion : float, optional
-        expansion parameter for ChannelMLP in FNO Block, by default 0.5
-    channel_mlp_skip : str {'linear', 'identity', 'soft-gating'}, optional
+    preactivation : bool, default is False
+        if True, use resnet-style preactivation
+    fno_skip : {'linear', 'identity', 'soft-gating'}, optional
+        Type of skip connection to use in fno, by default 'linear'
+    channel_mlp_skip : {'linear', 'identity', 'soft-gating'}, optional
         Type of skip connection to use in channel-mixing mlp, by default 'soft-gating'
-    fno_skip : str {'linear', 'identity', 'soft-gating'}, optional
-        Type of skip connection to use in FNO layers, by default 'linear'
-    resolution_scaling_factor : Union[Number, List[Number]], optional
-        layer-wise factor by which to scale the domain resolution of function, by default None
-        
-        * If a single number n, scales resolution by n at each layer
-
-        * if a list of numbers [n_0, n_1,...] scales layer i's resolution by n_i.
-    domain_padding : Union[Number, List[Number]], optional
+    separable : bool, default is False
+        if True, use a depthwise separable spectral convolution
+    factorization : str or None, {'tucker', 'cp', 'tt'}
+        Tensor factorization of the parameters weight to use, by default None.
+        * If None, a dense tensor parametrizes the Spectral convolutions
+        * Otherwise, the specified tensor factorization is used.
+    joint_factorization : bool, optional
+        Whether all the Fourier Layers should be parametrized by a single tensor
+        (vs one per layer), by default False
+    rank : float or rank, optional
+        Rank of the tensor factorization of the Fourier weights, by default 1.0
+    fixed_rank_modes : bool, optional
+        Modes to not factorize, by default False
+    implementation : {'factorized', 'reconstructed'}, optional, default is 'factorized'
+        If factorization is not None, forward mode to use::
+        * `reconstructed` : the full weight tensor is reconstructed from the
+          factorization and used for the forward pass
+        * `factorized` : the input is directly contracted with the factors of
+          the decomposition
+    decomposition_kwargs : dict, optional, default is {}
+        Optionaly additional parameters to pass to the tensor decomposition
+    domain_padding : None, float, or List[float], optional
         If not None, percentage of padding to use, by default None
         To vary the percentage of padding used along each input dimension,
         pass in a list of percentages e.g. [p1, p2, ..., pN] such that
         p1 corresponds to the percentage of padding along dim 1, etc.
-    domain_padding_mode : str {'symmetric', 'one-sided'}, optional
+    domain_padding_mode : {'symmetric', 'one-sided'}, optional
         How to perform domain padding, by default 'one-sided'
-    fno_block_precision : str {'full', 'half', 'mixed'}, optional
-        precision mode in which to perform spectral convolution, by default "full"
-    stabilizer : str {'tanh'} | None, optional
-        whether to use a tanh stabilizer in FNO block, by default None
-
-        Note: stabilizer greatly improves performance in the case
-        `fno_block_precision='mixed'`. 
-
-    max_n_modes : Tuple[int] | None, optional
-
-        * If not None, this allows to incrementally increase the number of
-        modes in Fourier domain during training. Has to verify n <= N
-        for (n, m) in zip(max_n_modes, n_modes).
-
-        * If None, all the n_modes are used.
-
-        This can be updated dynamically during training.
-    factorization : str, optional
-        Tensor factorization of the FNO layer weights to use, by default None.
-
-        * If None, a dense tensor parametrizes the Spectral convolutions
-
-        * Otherwise, the specified tensor factorization is used.
-    rank : float, optional
-        tensor rank to use in above factorization, by default 1.0
-    fixed_rank_modes : bool, optional
-        Modes to not factorize, by default False
-    implementation : str {'factorized', 'reconstructed'}, optional
-
-        * If 'factorized', implements tensor contraction with the individual factors of the decomposition 
-        
-        * If 'reconstructed', implements with the reconstructed full tensorized weight.
-    decomposition_kwargs : dict, optional
-        extra kwargs for tensor decomposition (see `tltorch.FactorizedTensor`), by default dict()
-    separable : bool, optional (**DEACTIVATED**)
-        if True, use a depthwise separable spectral convolution, by default False   
-    preactivation : bool, optional (**DEACTIVATED**)
-        whether to compute FNO forward pass with resnet-style preactivation, by default False
-    conv_module : nn.Module, optional
-        module to use for FNOBlock's convolutions, by default SpectralConv
+    conv_module : BaseConv, optional
+        Module to use for convolutions in FNO, by default SpectralConv
+    complex_data: bool, optional
+        whether FNO data takes on complex values 
+        in the spatial domain, by default False
     """
 
     def __init__(
         self,
-        n_modes: Tuple[int],
-        in_channels: int,
-        out_channels: int,
-        hidden_channels: int,
-        n_layers: int=4,
-        lifting_channel_ratio: int=2,
-        projection_channel_ratio: int=2,
-        positional_embedding: Union[str, nn.Module]="grid",
-        non_linearity: nn.Module=F.gelu,
-        norm: str=None,
-        complex: bool=False,
-        channel_mlp_dropout: float=0,
-        channel_mlp_expansion: float=0.5,
-        channel_mlp_skip: str="soft-gating",
-        fno_skip: str="linear",
-        resolution_scaling_factor: Union[Number, List[Number]]=None,
-        domain_padding: Union[Number, List[Number]]=None,
-        domain_padding_mode: str="one-sided",
-        fno_block_precision: str="full",
-        stabilizer: str=None,
-        max_n_modes: Tuple[int]=None,
-        factorization: str=None,
-        rank: float=1.0,
-        fixed_rank_modes: bool=False,
-        implementation: str="factorized",
-        decomposition_kwargs: dict=dict(),
-        separable: bool=False,
-        preactivation: bool=False,
-        conv_module: nn.Module=SpectralConv,
+        n_modes,
+        hidden_channels,
+        in_channels=3,
+        out_channels=1,
+        lifting_channels=256,
+        projection_channels=256,
+        n_layers=4,
+        positional_embedding="grid",
+        output_scaling_factor=None,
+        max_n_modes=None,
+        fno_block_precision="full",
+        use_channel_mlp=False,
+        channel_mlp_dropout=0,
+        channel_mlp_expansion=0.5,
+        non_linearity=F.gelu,
+        stabilizer=None,
+        norm=None,
+        preactivation=False,
+        fno_skip="linear",
+        channel_mlp_skip="soft-gating",
+        separable=False,
+        factorization=None,
+        rank=1.0,
+        joint_factorization=False,
+        fixed_rank_modes=False,
+        implementation="factorized",
+        decomposition_kwargs=dict(),
+        domain_padding=None,
+        domain_padding_mode="one-sided",
+        conv_module=SpectralConv,
+        complex_data=False,
         **kwargs
     ):
-        
         super().__init__()
         self.n_dim = len(n_modes)
-        
-        # n_modes is a special property - see the class' property for underlying mechanism
+
+        # See the class' property for underlying mechanism
         # When updated, change should be reflected in fno blocks
         self._n_modes = n_modes
-
         self.hidden_channels = hidden_channels
+        self.lifting_channels = lifting_channels
+        self.projection_channels = projection_channels
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.n_layers = n_layers
-
-        # init lifting and projection channels using ratios w.r.t hidden channels
-        self.lifting_channel_ratio = lifting_channel_ratio
-        self.lifting_channels = lifting_channel_ratio * self.hidden_channels
-
-        self.projection_channel_ratio = projection_channel_ratio
-        self.projection_channels = projection_channel_ratio * self.hidden_channels
-
+        self.joint_factorization = joint_factorization
         self.non_linearity = non_linearity
         self.rank = rank
         self.factorization = factorization
@@ -330,9 +278,8 @@ 
    Source code for neuralop.models.fno
             self.implementation = implementation
         self.separable = separable
         self.preactivation = preactivation
-        self.complex = complex
         self.fno_block_precision = fno_block_precision
-        
+
         if positional_embedding == "grid":
             spatial_grid_boundaries = [[0., 1.]] * self.n_dim
             self.positional_embedding = GridEmbeddingND(dim=self.n_dim, grid_boundaries=spatial_grid_boundaries)
@@ -356,24 +303,25 @@ 
    Source code for neuralop.models.fno
                 self.domain_padding = DomainPadding(
                 domain_padding=domain_padding,
                 padding_mode=domain_padding_mode,
-                resolution_scaling_factor=resolution_scaling_factor,
+                output_scaling_factor=output_scaling_factor,
             )
         else:
             self.domain_padding = None
 
         self.domain_padding_mode = domain_padding_mode
-        self.complex = self.complex
+        self.complex_data = complex_data
 
-        if resolution_scaling_factor is not None:
-            if isinstance(resolution_scaling_factor, (float, int)):
-                resolution_scaling_factor = [resolution_scaling_factor] * self.n_layers
-        self.resolution_scaling_factor = resolution_scaling_factor
+        if output_scaling_factor is not None and not joint_factorization:
+            if isinstance(output_scaling_factor, (float, int)):
+                output_scaling_factor = [output_scaling_factor] * self.n_layers
+        self.output_scaling_factor = output_scaling_factor
 
         self.fno_blocks = FNOBlocks(
             in_channels=hidden_channels,
             out_channels=hidden_channels,
             n_modes=self.n_modes,
-            resolution_scaling_factor=resolution_scaling_factor,
+            output_scaling_factor=output_scaling_factor,
+            use_channel_mlp=use_channel_mlp,
             channel_mlp_dropout=channel_mlp_dropout,
             channel_mlp_expansion=channel_mlp_expansion,
             non_linearity=non_linearity,
@@ -382,7 +330,7 @@ 
    Source code for neuralop.models.fno
                 preactivation=preactivation,
             fno_skip=fno_skip,
             channel_mlp_skip=channel_mlp_skip,
-            complex=complex,
+            complex_data=complex_data,
             max_n_modes=max_n_modes,
             fno_block_precision=fno_block_precision,
             rank=rank,
@@ -391,6 +339,7 @@ 
    Source code for neuralop.models.fno
                 separable=separable,
             factorization=factorization,
             decomposition_kwargs=decomposition_kwargs,
+            joint_factorization=joint_factorization,
             conv_module=conv_module,
             n_layers=n_layers,
             **kwargs
@@ -421,8 +370,8 @@ 
    Source code for neuralop.models.fno
                     n_dim=self.n_dim,
                 non_linearity=non_linearity
             )
-        # Convert lifting to a complex ChannelMLP if self.complex==True
-        if self.complex:
+        # Convert lifting to a complex ChannelMLP if self.complex_data==True
+        if self.complex_data:
             self.lifting = ComplexValued(self.lifting)
 
         self.projection = ChannelMLP(
@@ -433,39 +382,22 @@ 
    Source code for neuralop.models.fno
                 n_dim=self.n_dim,
             non_linearity=non_linearity,
         )
-        if self.complex:
+        if self.complex_data:
             self.projection = ComplexValued(self.projection)
 
 
    
 [docs]
     def forward(self, x, output_shape=None, **kwargs):
-        """FNO's forward pass
-        
-        1. Applies optional positional encoding
-
-        2. Sends inputs through a lifting layer to a high-dimensional latent
-            space
-
-        3. Applies optional domain padding to high-dimensional intermediate function representation
-
-        4. Applies `n_layers` Fourier/FNO layers in sequence (SpectralConvolution + skip connections, nonlinearity) 
-
-        5. If domain padding was applied, domain padding is removed
-
-        6. Projection of intermediate function representation to the output channels
+        """TFNO's forward pass
 
         Parameters
         ----------
         x : tensor
             input tensor
-        
         output_shape : {tuple, tuple list, None}, default is None
             Gives the option of specifying the exact output shape for odd shaped inputs.
-            
             * If None, don't specify an output shape
-
             * If tuple, specifies the output-shape of the **last** FNO Block
-
             * If tuple list, specifies the exact output-shape of each FNO Block
         """
 
@@ -527,12 +459,12 @@ 
    Source code for neuralop.models.fno
             lifting_channels=256,
         projection_channels=256,
         max_n_modes=None,
+        fno_block_precision="full",
         n_layers=4,
-        resolution_scaling_factor=None,
+        output_scaling_factor=None,
         non_linearity=F.gelu,
         stabilizer=None,
-        complex=torch.float32,
-        fno_block_precision="full",
+        use_channel_mlp=False,
         channel_mlp_dropout=0,
         channel_mlp_expansion=0.5,
         norm=None,
@@ -541,6 +473,7 @@ 
    Source code for neuralop.models.fno
             preactivation=False,
         factorization=None,
         rank=1.0,
+        joint_factorization=False,
         fixed_rank_modes=False,
         implementation="factorized",
         decomposition_kwargs=dict(),
@@ -556,20 +489,21 @@ 
    Source code for neuralop.models.fno
                 lifting_channels=lifting_channels,
             projection_channels=projection_channels,
             n_layers=n_layers,
-            resolution_scaling_factor=resolution_scaling_factor,
+            output_scaling_factor=output_scaling_factor,
             non_linearity=non_linearity,
             stabilizer=stabilizer,
-            complex=complex,
-            fno_block_precision=fno_block_precision,
+            use_channel_mlp=use_channel_mlp,
             channel_mlp_dropout=channel_mlp_dropout,
             channel_mlp_expansion=channel_mlp_expansion,
             max_n_modes=max_n_modes,
+            fno_block_precision=fno_block_precision,
             norm=norm,
             skip=skip,
             separable=separable,
             preactivation=preactivation,
             factorization=factorization,
             rank=rank,
+            joint_factorization=joint_factorization,
             fixed_rank_modes=fixed_rank_modes,
             implementation=implementation,
             decomposition_kwargs=decomposition_kwargs,
@@ -605,12 +539,12 @@ 
    Source code for neuralop.models.fno
             lifting_channels=256,
         projection_channels=256,
         n_layers=4,
-        resolution_scaling_factor=None,
+        output_scaling_factor=None,
         max_n_modes=None,
+        fno_block_precision="full",
         non_linearity=F.gelu,
         stabilizer=None,
-        complex=torch.float32,
-        fno_block_precision="full",
+        use_channel_mlp=False,
         channel_mlp_dropout=0,
         channel_mlp_expansion=0.5,
         norm=None,
@@ -619,6 +553,7 @@ 
    Source code for neuralop.models.fno
             preactivation=False,
         factorization=None,
         rank=1.0,
+        joint_factorization=False,
         fixed_rank_modes=False,
         implementation="factorized",
         decomposition_kwargs=dict(),
@@ -634,20 +569,21 @@ 
    Source code for neuralop.models.fno
                 lifting_channels=lifting_channels,
             projection_channels=projection_channels,
             n_layers=n_layers,
-            resolution_scaling_factor=resolution_scaling_factor,
+            output_scaling_factor=output_scaling_factor,
             non_linearity=non_linearity,
             stabilizer=stabilizer,
-            complex=complex,
-            fno_block_precision=fno_block_precision,
+            use_channel_mlp=use_channel_mlp,
             channel_mlp_dropout=channel_mlp_dropout,
             channel_mlp_expansion=channel_mlp_expansion,
             max_n_modes=max_n_modes,
+            fno_block_precision=fno_block_precision,
             norm=norm,
             skip=skip,
             separable=separable,
             preactivation=preactivation,
             factorization=factorization,
             rank=rank,
+            joint_factorization=joint_factorization,
             fixed_rank_modes=fixed_rank_modes,
             implementation=implementation,
             decomposition_kwargs=decomposition_kwargs,
@@ -687,12 +623,12 @@ 
    Source code for neuralop.models.fno
             lifting_channels=256,
         projection_channels=256,
         n_layers=4,
-        resolution_scaling_factor=None,
+        output_scaling_factor=None,
         max_n_modes=None,
+        fno_block_precision="full",
         non_linearity=F.gelu,
         stabilizer=None,
-        complex=torch.float32,
-        fno_block_precision="full",
+        use_channel_mlp=False,
         channel_mlp_dropout=0,
         channel_mlp_expansion=0.5,
         norm=None,
@@ -701,6 +637,7 @@ 
    Source code for neuralop.models.fno
             preactivation=False,
         factorization=None,
         rank=1.0,
+        joint_factorization=False,
         fixed_rank_modes=False,
         implementation="factorized",
         decomposition_kwargs=dict(),
@@ -716,12 +653,12 @@ 
    Source code for neuralop.models.fno
                 lifting_channels=lifting_channels,
             projection_channels=projection_channels,
             n_layers=n_layers,
-            resolution_scaling_factor=resolution_scaling_factor,
+            output_scaling_factor=output_scaling_factor,
             non_linearity=non_linearity,
             stabilizer=stabilizer,
-            complex=complex,
-            fno_block_precision=fno_block_precision,
             max_n_modes=max_n_modes,
+            fno_block_precision=fno_block_precision,
+            use_channel_mlp=use_channel_mlp,
             channel_mlp_dropout=channel_mlp_dropout,
             channel_mlp_expansion=channel_mlp_expansion,
             norm=norm,
@@ -730,6 +667,7 @@ 
    Source code for neuralop.models.fno
                 preactivation=preactivation,
             factorization=factorization,
             rank=rank,
+            joint_factorization=joint_factorization,
             fixed_rank_modes=fixed_rank_modes,
             implementation=implementation,
             decomposition_kwargs=decomposition_kwargs,
diff --git a/dev/_modules/neuralop/models/gino.html b/dev/_modules/neuralop/models/gino.html
index 56135e7..97ccc44 100644
--- a/dev/_modules/neuralop/models/gino.html
+++ b/dev/_modules/neuralop/models/gino.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
@@ -183,7 +183,7 @@ 
    Source code for neuralop.models.gino
                 number of channels in FNO's pointwise projection, by default 256
         fno_n_layers : int, optional
             number of layers in FNO, by default 4
-        fno_resolution_scaling_factor : float | None, optional
+        fno_output_scaling_factor : float | None, optional
             factor by which to scale output of FNO, by default None
         fno_incremental_n_modes : list[int] | None, defaults to None
         if passed, sets n_modes separately for each FNO layer.
@@ -255,7 +255,7 @@ 
    Source code for neuralop.models.gino
                 fno_hidden_channels=64,
             lifting_channels=256,
             fno_n_layers=4,
-            fno_resolution_scaling_factor=None,
+            fno_output_scaling_factor=None,
             fno_incremental_n_modes=None,
             fno_block_precision='full',
             fno_use_channel_mlp=False, 
@@ -328,7 +328,7 @@ 
    Source code for neuralop.models.gino
                     out_channels=fno_hidden_channels,
                 positional_embedding=None,
                 n_layers=fno_n_layers,
-                resolution_scaling_factor=fno_resolution_scaling_factor,
+                output_scaling_factor=fno_output_scaling_factor,
                 incremental_n_modes=fno_incremental_n_modes,
                 fno_block_precision=fno_block_precision,
                 use_channel_mlp=fno_use_channel_mlp,
diff --git a/dev/_modules/neuralop/models/uno.html b/dev/_modules/neuralop/models/uno.html
index 5f187e1..26721cc 100644
--- a/dev/_modules/neuralop/models/uno.html
+++ b/dev/_modules/neuralop/models/uno.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
@@ -173,10 +173,11 @@ 
    Source code for neuralop.models.uno
             * If None, all the n_modes are used.
 
         This can be updated dynamically during training.
-    channel_mlp_dropout: float, optional
-        dropout parameter for channelMLP after each FNO Block
-    channel_mlp_expansions: float, optional
-        expansion parameter for channelMLP after each FNO block
+    use_channel_mlp : bool, optional
+        Whether to use an ChannelMLP layer after each FNO block, by default False
+    ChannelMLP : dict, optional
+        Parameters of the ChannelMLP, by default None
+        {'expansion': float, 'dropout': float}
     non_linearity : nn.Module, optional
         Non-Linearity module to use, by default F.gelu
     norm : F.module, optional
@@ -227,17 +228,19 @@ 
    Source code for neuralop.models.uno
             uno_scalings=None,
         horizontal_skips_map=None,
         incremental_n_modes=None,
-        channel_mlp_dropout=0,
-        channel_mlp_expansion=0.5,
+        use_channel_mlp=False,
+        channel_mlpdropout=0,
+        channel_mlpexpansion=0.5,
         non_linearity=F.gelu,
         norm=None,
         preactivation=False,
         fno_skip="linear",
         horizontal_skip="linear",
-        channel_mlp_skip="soft-gating",
+        channel_mlpskip="soft-gating",
         separable=False,
         factorization=None,
         rank=1.0,
+        joint_factorization=False,
         fixed_rank_modes=False,
         integral_operator=SpectralConv,
         operator_block=FNOBlocks,
@@ -245,6 +248,8 @@ 
    Source code for neuralop.models.uno
             decomposition_kwargs=dict(),
         domain_padding=None,
         domain_padding_mode="one-sided",
+        fft_norm="forward",
+        normalizer=None,
         verbose=False,
         **kwargs
     ):
@@ -273,13 +278,15 @@ 
    Source code for neuralop.models.uno
             self.in_channels = in_channels
         self.out_channels = out_channels
         self.horizontal_skips_map = horizontal_skips_map
+        self.joint_factorization = joint_factorization
         self.non_linearity = non_linearity
         self.rank = rank
         self.factorization = factorization
         self.fixed_rank_modes = fixed_rank_modes
         self.decomposition_kwargs = decomposition_kwargs
         self.fno_skip = (fno_skip,)
-        self.channel_mlp_skip = (channel_mlp_skip,)
+        self.channel_mlpskip = (channel_mlpskip,)
+        self.fft_norm = fft_norm
         self.implementation = implementation
         self.separable = separable
         self.preactivation = preactivation
@@ -347,7 +354,7 @@ 
    Source code for neuralop.models.uno
                 self.domain_padding = DomainPadding(
                 domain_padding=domain_padding,
                 padding_mode=domain_padding_mode,
-                resolution_scaling_factor=self.end_to_end_scaling_factor,
+                output_scaling_factor=self.end_to_end_scaling_factor,
             )
         else:
             self.domain_padding = None
@@ -370,29 +377,31 @@ 
    Source code for neuralop.models.uno
                         prev_out + self.uno_out_channels[self.horizontal_skips_map[i]]
                 )
 
-            print(f"{fno_skip=}")
-            print(f"{channel_mlp_skip=}")
             self.fno_blocks.append(
                 self.operator_block(
                     in_channels=prev_out,
                     out_channels=self.uno_out_channels[i],
                     n_modes=self.uno_n_modes[i],
-                    channel_mlp_dropout=channel_mlp_dropout,
-                    channel_mlp_expansion=channel_mlp_expansion,
-                    resolution_scaling_factor=[self.uno_scalings[i]],
+                    use_channel_mlp=use_channel_mlp,
+                    channel_mlpdropout=channel_mlpdropout,
+                    channel_mlpexpansion=channel_mlpexpansion,
+                    output_scaling_factor=[self.uno_scalings[i]],
                     non_linearity=non_linearity,
                     norm=norm,
                     preactivation=preactivation,
                     fno_skip=fno_skip,
-                    channel_mlp_skip=channel_mlp_skip,
+                    channel_mlpskip=channel_mlpskip,
                     incremental_n_modes=incremental_n_modes,
                     rank=rank,
                     SpectralConv=self.integral_operator,
+                    fft_norm=fft_norm,
                     fixed_rank_modes=fixed_rank_modes,
                     implementation=implementation,
                     separable=separable,
                     factorization=factorization,
                     decomposition_kwargs=decomposition_kwargs,
+                    joint_factorization=joint_factorization,
+                    normalizer=normalizer,
                 )
             )
 
@@ -436,12 +445,12 @@ 
    Source code for neuralop.models.uno
                 
             if layer_idx in self.horizontal_skips_map.keys():
                 skip_val = skip_outputs[self.horizontal_skips_map[layer_idx]]
-                resolution_scaling_factors = [
+                output_scaling_factors = [
                     m / n for (m, n) in zip(x.shape, skip_val.shape)
                 ]
-                resolution_scaling_factors = resolution_scaling_factors[-1 * self.n_dim :]
+                output_scaling_factors = output_scaling_factors[-1 * self.n_dim :]
                 t = resample(
-                    skip_val, resolution_scaling_factors, list(range(-self.n_dim, 0))
+                    skip_val, output_scaling_factors, list(range(-self.n_dim, 0))
                 )
                 x = torch.cat([x, t], dim=1)
 
diff --git a/dev/_modules/neuralop/training/callbacks.html b/dev/_modules/neuralop/training/callbacks.html
deleted file mode 100644
index ac3eccf..0000000
--- a/dev/_modules/neuralop/training/callbacks.html
+++ /dev/null
@@ -1,721 +0,0 @@
-
-
-
-  
-    
-    
-    neuralop.training.callbacks — neuraloperator 0.3.0 documentation 
-
-
-
-
-
-
-
-
-
-    
-    
-    
-    
-    
-    
-
-  
-    
-    
-    
- 
- 
- 
-    
-    
-
-
-  
-
-
-  
    
-    
-      
-      
-    
-  
    
-
-
-  
    
-  
    
-	
-  
-
-  
    
-
-    
-    
    
-
-      
-      
-
-      
    
-        
-  
    Source code for neuralop.training.callbacks
    -"""
-Callbacks store all non-essential logic
-required to run specific training scripts. 
-
-The callbacks in this module follow the form and 
-logic of callbacks in Pytorch-Lightning (https://lightning.ai/docs/pytorch/stable)
-"""
-
-import os
-from pathlib import Path
-import sys
-from typing import List, Union, Literal
-
-import torch
-import wandb
-
-from .training_state import save_training_state, load_training_state
-from neuralop.utils import compute_rank, compute_stable_rank, compute_explained_variance
-
-
-
    
-[docs]
-class Callback(object):
-    """
-    Base callback class. Each abstract method is called in the trainer's
-    training loop at the appropriate time.
-
-    Callbacks are stateful, meaning they keep track of a state and
-        update it throughout the lifetime of a Trainer class.
-        Storing the state as a dict enables the Callback to keep track of
-        references to underlying parts of the Trainer's process, such as
-        models, cost functions and output encoders
-    """
-
-    def __init__(self):
-        self.state_dict = {}
-
-    def _update_state_dict(self, **kwargs):
-        self.state_dict.update(kwargs)
-
-    def on_init_start(self, **kwargs):
-        pass
-
-    def on_init_end(self, *args, **kwargs):
-        pass
-
-    def on_before_train(self, *args, **kwargs):
-        pass
-
-    def on_train_start(self, *args, **kwargs):
-        pass
-
-    def on_epoch_start(self, *args, **kwargs):
-        pass
-
-    def on_batch_start(self, *args, **kwargs):
-        pass
-
-    def on_load_to_device(self, *args, **kwargs):
-        pass
-
-    def on_before_forward(self, *args, **kwargs):
-        pass
-
-    def on_before_loss(self, *args, **kwargs):
-        pass
-
-    def compute_training_loss(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def on_batch_end(self, *args, **kwargs):
-        pass
-
-    def on_epoch_end(self, *args, **kwargs):
-        pass
-
-    def on_train_end(self, *args, **kwargs):
-        pass
-
-    def on_before_val(self, *args, **kwargs):
-        pass
-
-    def on_val_epoch_start(self, *args, **kwargs):
-        pass
-
-    def on_val_batch_start(self, *args, **kwargs):
-        pass
-
-    def on_before_val_loss(self, **kwargs):
-        pass
-
-    def compute_val_loss(self, *args, **kwargs):
-        pass
-
-    def on_val_batch_end(self, *args, **kwargs):
-        pass
-
-    def on_val_epoch_end(self, *args, **kwargs):
-        pass
-
-    def on_val_end(self, *args, **kwargs):
-        pass
    
-
-
-
-class PipelineCallback(Callback):
-    def __init__(self, callbacks: List[Callback]):
-        """
-        PipelineCallback handles logic for the case in which
-        a user passes more than one Callback to a trainer.
-
-        Parameters
-        ----------
-        callbacks : List[Callback]
-            list of Callbacks to use in Trainer
-        """
-        self.callbacks = callbacks
-
-        overrides_device_load = [
-            "on_load_to_device" in c.__class__.__dict__.keys() for c in callbacks
-        ]
-
-        assert (
-            sum(overrides_device_load) < 2
-        ), "More than one callback cannot override device loading"
-        if sum(overrides_device_load) == 1:
-            self.device_load_callback_idx = overrides_device_load.index(True)
-            print("using custom callback to load data to device.")
-        else:
-            self.device_load_callback_idx = None
-            print("using standard method to load data to device.")
-
-        # unless loss computation is overriden, call a basic loss function calculation
-        overrides_loss = [
-            "compute_training_loss" in c.__class__.__dict__.keys() for c in callbacks
-        ]
-
-        if sum(overrides_loss) >= 1:
-            self.overrides_loss = True
-            print("using custom callback to compute loss.")
-        else:
-            self.overrides_loss = False
-            print("using standard method to compute loss.")
-
-    def _update_state_dict(self, **kwargs):
-        for c in self.callbacks:
-            c._update_state_dict(kwargs)
-
-    def on_init_start(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_init_start(*args, **kwargs)
-
-    def on_init_end(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_init_end(*args, **kwargs)
-
-    def on_before_train(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_before_train(*args, **kwargs)
-
-    def on_train_start(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_train_start(*args, **kwargs)
-
-    def on_epoch_start(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_epoch_start(*args, **kwargs)
-
-    def on_batch_start(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_batch_start(*args, **kwargs)
-
-    def on_load_to_device(self, *args, **kwargs):
-        if self.device_load_callback_idx:
-            self.callbacks[self.device_load_callback_idx].on_load_to_device(
-                *args, *kwargs
-            )
-
-    def on_before_forward(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_before_forward(*args, **kwargs)
-
-    def on_before_loss(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_before_loss(*args, **kwargs)
-
-    def compute_training_loss(self, *args, **kwargs):
-        if self.overrides_loss:
-            for c in self.callbacks:
-                c.compute_training_loss(*args, **kwargs)
-        else:
-            pass
-
-    def on_batch_end(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_batch_end(*args, **kwargs)
-
-    def on_epoch_end(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_epoch_end(*args, **kwargs)
-
-    def on_train_end(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_train_end(*args, **kwargs)
-
-    def on_before_val(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_before_val(*args, **kwargs)
-
-    def on_val_epoch_start(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_val_epoch_start(*args, **kwargs)
-
-    def on_val_batch_start(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_val_batch_start(*args, **kwargs)
-
-    def on_before_val_loss(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_before_val_loss(*args, **kwargs)
-
-    def compute_val_loss(self, *args, **kwargs):
-        if self.overrides_loss:
-            for c in self.callbacks:
-                c.compute_val_loss(*args, **kwargs)
-        else:
-            pass
-
-    def on_val_batch_end(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_val_batch_end(*args, **kwargs)
-
-    def on_val_epoch_end(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_val_epoch_end(*args, **kwargs)
-
-    def on_val_end(self, *args, **kwargs):
-        for c in self.callbacks:
-            c.on_val_end(*args, **kwargs)
-
-
    
-[docs]
-class CheckpointCallback(Callback):
-    def __init__(
-        self,
-        save_dir: Union[Path, str],
-        save_best: str = None,
-        save_interval: int = 1,
-        save_optimizer: bool = False,
-        save_scheduler: bool = False,
-        save_regularizer: bool = False,
-        resume_from_dir: Union[Path, str] = None,
-    ):
-        """CheckpointCallback handles saving and resuming
-        training state from checkpoint .pt save files.
-
-        Parameters
-        ----------
-        save_dir : Union[Path, str], optional
-            folder in which to save checkpoints, by default './checkpoints'
-        save_best : str, optional
-            metric to monitor for best value in order to save state
-        save_interval : int, optional
-            interval on which to save/check metric, by default 1
-        save_optimizer : bool, optional
-            whether to save optimizer state, by default False
-        save_scheduler : bool, optional
-            whether to save scheduler state, by default False
-        save_regularizer : bool, optional
-            whether to save regularizer state, by default False
-        resume_from_dir : Union[Path, str], optional
-            folder from which to resume training state.
-            Expects saved states in the form: (all but model optional)
-               (best_model.pt or model.pt), optimizer.pt, scheduler.pt, regularizer.pt
-            All state files present will be loaded.
-            if some metric was monitored during checkpointing,
-            the file name will be best_model.pt.
-        """
-
-        super().__init__()
-        if isinstance(save_dir, str):
-            save_dir = Path(save_dir)
-        if not save_dir.exists():
-            save_dir.mkdir(parents=True)
-        self.save_dir = save_dir
-
-        self.save_interval = save_interval
-        self.save_best = save_best
-        self.save_optimizer = save_optimizer
-        self.save_scheduler = save_scheduler
-        self.save_regularizer = save_regularizer
-
-        if resume_from_dir:
-            if isinstance(resume_from_dir, str):
-                resume_from_dir = Path(resume_from_dir)
-            assert resume_from_dir.exists()
-
-        self.resume_from_dir = resume_from_dir
-
-    def on_init_end(self, *args, **kwargs):
-        self._update_state_dict(**kwargs)
-
-    def on_train_start(self, *args, **kwargs):
-        self._update_state_dict(**kwargs)
-
-        verbose = self.state_dict.get("verbose", False)
-        if self.save_best:
-            assert self.state_dict[
-                "eval_losses"
-            ], "Error: cannot monitor a metric if no validation metrics exist."
-            assert (
-                self.save_best in self.state_dict["eval_losses"].keys()
-            ), "Error: cannot monitor a metric outside of eval_losses."
-            self.best_metric_value = float("inf")
-        else:
-            self.best_metric_value = None
-
-        # load state dict if resume_from_dir is given
-        if self.resume_from_dir:
-            saved_modules = [x.stem for x in self.resume_from_dir.glob("*.pt")]
-
-            # check for save model exists
-            if (self.resume_from_dir / "best_model_state_dict.pt").exists():
-                save_name = "best_model"
-            elif (self.resume_from_dir / "model_state_dict.pt").exists():
-                save_name = "model"
-            else:
-                raise FileNotFoundError("Error: CheckpointCallback expects a model\
-                                         state dict named model.pt or best_model.pt.")
-            # returns key-value pairs "model":model, "optimizer":optimizer...
-            training_state = load_training_state(save_dir=self.resume_from_dir, save_name=save_name,
-                                                 model=self.state_dict['model'],
-                                                 optimizer=self.state_dict.get('optimizer'),
-                                                 regularizer=self.state_dict.get('regularizer'),
-                                                 scheduler=self.state_dict.get('scheduler'))
-            
-            self._update_state_dict(**training_state)
-            
-    def on_epoch_start(self, *args, **kwargs):
-        self._update_state_dict(**kwargs)
-
-    def on_val_epoch_start(self, *args, **kwargs):
-        self._update_state_dict(**kwargs)
-
-
    
-[docs]
-    def on_val_epoch_end(self, *args, **kwargs):
-        """
-        Update state dict with errors
-        """
-        self._update_state_dict(**kwargs)
    
-
-
-
    
-[docs]
-    def on_epoch_end(self, *args, **kwargs):
-        """
-        Save state to dir if all conditions are met
-        """
-        if self.save_best:
-            log_prefix = self.state_dict["log_prefix"]
-            if (
-                self.state_dict["errors"][f"{log_prefix}_{self.save_best}"]
-                < self.best_metric_value
-            ):
-                metric_cond = True
-            else:
-                metric_cond = False
-        else:
-            metric_cond = True
-
-        # Save states to save_dir
-        if self.state_dict["epoch"] % self.save_interval == 0 and metric_cond:
-            # save model or best_model.pt no matter what
-            if self.save_best:
-                model_name = "best_model"
-            else:
-                model_name = "model"
-
-            save_training_state(
-                self.save_dir,
-                model_name,
-                model=self.state_dict["model"],
-                optimizer=self.state_dict.get("optimizer", None),
-                regularizer=self.state_dict.get("regularizer", None),
-                scheduler=self.state_dict.get("scheduler", None),
-            )
-
-            if self.state_dict["verbose"]:
-                print(f"Saved training state to {self.save_dir}")
    
-
    
-
-
-class IncrementalCallback(Callback):
-    """
-    Callback that implements the Incremental Algorithm - Both the Gradient explained and Loss Gap versions
-    
-    incremental : bool, default is False
-        if True, use the base incremental algorithm which is based on gradient variance
-        uses the incremental_grad_eps parameter - set the threshold for gradient variance
-        uses the incremental_buffer paramater - sets the number of buffer modes to calculate the gradient variance
-        uses the incremental_max_iter parameter - sets the initial number of iterations
-        uses the incremental_grad_max_iter parameter - sets the maximum number of iterations to accumulate the gradients
-    incremental_loss_gap : bool, default is False
-        if True, use the incremental algorithm based on loss gap
-        uses the incremental_loss_eps parameter
-    """
-
-    def __init__(self,
-                 incremental_grad: bool = False, 
-                 incremental_loss_gap: bool = False, 
-                 incremental_grad_eps: float = 0.001,
-                 incremental_buffer: int = 5, 
-                 incremental_max_iter: int = 1, 
-                 incremental_grad_max_iter: int = 10,
-                 incremental_loss_eps: float = 0.001
-                 ):
-        super().__init__()
-        self.incremental_loss_gap = incremental_loss_gap
-        self.incremental_grad = incremental_grad
-        self.incremental = self.incremental_loss_gap or self.incremental_grad
-        assert self.incremental, "Error: IncrementalCallback expects at least one incremental algorithm to be True."
-        assert not (self.incremental_loss_gap and self.incremental_grad), "Error: IncrementalCallback expects only one incremental algorithm to be True."
-        
-        self.incremental_grad_eps = incremental_grad_eps
-        self.incremental_buffer = incremental_buffer
-        self.incremental_max_iter = incremental_max_iter
-        self.incremental_grad_max_iter = incremental_grad_max_iter
-        self.incremental_loss_eps = incremental_loss_eps
-        self.loss_list = []
-        self.mode = "Train"
-    
-    def on_init_end(self, *args, **kwargs):
-        self._update_state_dict(**kwargs)
-    
-    def on_train_start(self, **kwargs):
-        self._update_state_dict(**kwargs)
-
-        train_loader = self.state_dict['train_loader']
-        test_loaders = self.state_dict['test_loaders']
-        verbose = self.state_dict['verbose']
-
-        n_train = len(train_loader.dataset)
-        self._update_state_dict(n_train=n_train)
-
-        if not isinstance(test_loaders, dict):
-            test_loaders = dict(test=test_loaders)
-
-        if verbose:
-            print(f'Training on {n_train} samples')
-            print(f'Testing on {[len(loader.dataset) for loader in test_loaders.values()]} samples'
-                  f'         on resolutions {[name for name in test_loaders]}.')
-            sys.stdout.flush()
-        
-    def on_epoch_start(self, epoch):
-        self._update_state_dict(epoch=epoch)
-        
-    def on_epoch_end(self, epoch, **kwargs):
-        self._update_state_dict(epoch=epoch)
-        print(f'Currently the model is using incremental_n_modes = {self.state_dict["model"].fno_blocks.convs.n_modes}')
-    
-    def on_batch_start(self, idx, **kwargs):
-        self._update_state_dict(idx=idx)
-        self.mode = "Train"
-        self.data = self.state_dict['data_processor']
-        if self.data is not None:
-            self.data.epoch = self.state_dict['epoch']
-        
-    def on_before_loss(self, out, **kwargs):
-        if self.state_dict['epoch'] == 0 and self.state_dict['idx'] == 0 \
-            and self.state_dict['verbose']:
-            print(f'Raw outputs of size {out.shape=}')
-    
-    def on_before_val(self, epoch, train_err, time, avg_loss, avg_lasso_loss, **kwargs):
-        # track training err and val losses to print at interval epochs
-        msg = f'[{epoch}] time={time:.2f}, avg_loss={avg_loss:.4f}, train_err={train_err:.4f}'
-
-        self.step(avg_loss)
-        
-        self._update_state_dict(msg=msg)
-        self._update_state_dict(avg_lasso_loss=avg_lasso_loss)
-        
-    def on_val_epoch_end(self, errors, **kwargs):
-        for loss_name, loss_value in errors.items():
-            if isinstance(loss_value, float):
-                self.state_dict['msg'] += f', {loss_name}={loss_value:.4f}'
-            else:
-                loss_value = {i:e.item() for (i, e) in enumerate(loss_value)}
-                self.state_dict['msg'] += f', {loss_name}={loss_value}'
-    
-    def on_val_batch_start(self, *args, **kwargs):
-        self.mode = "Validation"
-        if self.data is not None:
-            self.data.epoch = self.state_dict['epoch']
-
-    def on_val_end(self, *args, **kwargs):
-        if self.state_dict.get('regularizer', False):
-            avg_lasso = self.state_dict.get('avg_lasso_loss', 0.)
-            avg_lasso /= self.state_dict.get('n_epochs')
-            self.state_dict['msg'] += f', avg_lasso={avg_lasso:.5f}'
-        
-        print(self.state_dict['msg'])
-        sys.stdout.flush()
-    
-    # Main step function: which algorithm to run
-    def step(self, loss=None):
-        if self.incremental_loss_gap and loss is not None:
-            self.loss_gap(loss)
-        if self.incremental_grad:
-            self.grad_explained()
-    
-    # Algorithm 1: Incremental
-    def loss_gap(self, loss):
-        self.loss_list.append(loss)
-        self.ndim = len(self.state_dict['model'].fno_blocks.convs.n_modes)
-        # method 1: loss_gap
-        incremental_modes = self.state_dict['model'].fno_blocks.convs.n_modes[0]
-        max_modes = self.state_dict['model'].fno_blocks.convs.max_n_modes[0]
-        if len(self.loss_list) > 1:
-            if abs(self.loss_list[-1] - self.loss_list[-2]) <= self.incremental_loss_eps:
-                if incremental_modes < max_modes:
-                    incremental_modes += 1
-        modes_list = tuple([incremental_modes] * self.ndim)
-        self.state_dict['model'].fno_blocks.convs.n_modes = modes_list
-
-    # Algorithm 2: Gradient based explained ratio
-    def grad_explained(self):
-        # for mode 1
-        if not hasattr(self, 'accumulated_grad'):
-            self.accumulated_grad = torch.zeros_like(
-                self.state_dict['model'].fno_blocks.convs.weight[0])
-        if not hasattr(self, 'grad_iter'):
-            self.grad_iter = 1
-            
-        self.ndim = len(self.state_dict['model'].fno_blocks.convs.n_modes)
-        if self.grad_iter <= self.incremental_grad_max_iter:
-            self.grad_iter += 1
-            self.accumulated_grad += self.state_dict['model'].fno_blocks.convs.weight[0]
-        else:
-            incremental_final = []
-            for i in range(self.ndim):
-                max_modes = self.state_dict['model'].fno_blocks.convs.max_n_modes[i]
-                incremental_modes = self.state_dict['model'].fno_blocks.convs.n_modes[i]
-                weight = self.accumulated_grad
-                strength_vector = []
-                for mode_index in range(
-                        min(weight.shape[1], incremental_modes)):
-                    strength = torch.norm(
-                        weight[:, mode_index, :], p='fro')
-                    strength_vector.append(strength)
-                expained_ratio = compute_explained_variance(
-                    incremental_modes - self.incremental_buffer, torch.Tensor(strength_vector))
-                if expained_ratio < self.incremental_grad_eps:
-                    if incremental_modes < max_modes:
-                        incremental_modes += 1
-                incremental_final.append(incremental_modes)
-
-            # update the modes and frequency dimensions
-            self.grad_iter = 1
-            self.accumulated_grad = torch.zeros_like(
-                self.state_dict['model'].fno_blocks.convs.weight[0])
-            main_modes = incremental_final[0]
-            modes_list = tuple([main_modes] * self.ndim)
-            self.state_dict['model'].fno_blocks.convs.n_modes = tuple(modes_list)
-
    
-
-      
    
-
-      
-
-        
    
-    
    
-        
    
-          © Copyright 2024, Jean Kossaifi, Nikola Kovachki, Zongyi Li and Anima Anandkumar.
    
-        
    
-    
    
-  
    
-
-    
    
-
-  
      
-
-	
-
-  
      
-  
     
-
-  
-   
-
-
-
-  
-
\ No newline at end of file
diff --git a/dev/_modules/neuralop/training/incremental.html b/dev/_modules/neuralop/training/incremental.html
index d2298fd..2a8afca 100644
--- a/dev/_modules/neuralop/training/incremental.html
+++ b/dev/_modules/neuralop/training/incremental.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
@@ -241,17 +241,17 @@ 
    Source code for neuralop.training.incremental
                scalar value of epoch's training loss
         """
         self.loss_list.append(loss)
-        self.ndim = len(self.model.fno_blocks.convs[0].n_modes)
+        self.ndim = len(self.model.fno_blocks.convs.n_modes)
 
         # method 1: loss_gap
-        incremental_modes = self.model.fno_blocks.convs[0].n_modes[0]
-        max_modes = self.model.fno_blocks.convs[0].max_n_modes[0]
+        incremental_modes = self.model.fno_blocks.convs.n_modes[0]
+        max_modes = self.model.fno_blocks.convs.max_n_modes[0]
         if len(self.loss_list) > 1:
             if abs(self.loss_list[-1] - self.loss_list[-2]) <= self.incremental_loss_eps:
                 if incremental_modes < max_modes:
                     incremental_modes += 1
         modes_list = tuple([incremental_modes] * self.ndim)
-        self.model.fno_blocks.convs[0].n_modes = modes_list
    
+        self.model.fno_blocks.convs.n_modes = modes_list
    
 
 
     # Algorithm 2: Gradient based explained ratio
@@ -259,19 +259,19 @@ 
    Source code for neuralop.training.incremental
    # for mode 1
         if not hasattr(self, 'accumulated_grad'):
             self.accumulated_grad = torch.zeros_like(
-                self.model.fno_blocks.convs[0].weight)
+                self.model.fno_blocks.convs.weight[0])
         if not hasattr(self, 'grad_iter'):
             self.grad_iter = 1
             
-        self.ndim = len(self.model.fno_blocks.convs[0].n_modes)
+        self.ndim = len(self.model.fno_blocks.convs.n_modes)
         if self.grad_iter <= self.incremental_grad_max_iter:
             self.grad_iter += 1
-            self.accumulated_grad += self.model.fno_blocks.convs[0].weight
+            self.accumulated_grad += self.model.fno_blocks.convs.weight[0]
         else:
             incremental_final = []
             for i in range(self.ndim):
-                max_modes = self.model.fno_blocks.convs[i].max_n_modes[0]
-                incremental_modes = self.model.fno_blocks.convs[0].n_modes[0]
+                max_modes = self.model.fno_blocks.convs.max_n_modes[i]
+                incremental_modes = self.model.fno_blocks.convs.n_modes[i]
                 weight = self.accumulated_grad
                 strength_vector = []
                 for mode_index in range(
@@ -289,10 +289,10 @@ 
    Source code for neuralop.training.incremental
    # update the modes and frequency dimensions
             self.grad_iter = 1
             self.accumulated_grad = torch.zeros_like(
-                self.model.fno_blocks.convs[0].weight)
+                self.model.fno_blocks.convs.weight[0])
             main_modes = incremental_final[0]
             modes_list = tuple([main_modes] * self.ndim)
-            self.model.fno_blocks.convs[0].n_modes = tuple(modes_list)
    
+            self.model.fno_blocks.convs.n_modes = tuple(modes_list)
    
 
         
 
    
diff --git a/dev/_modules/neuralop/training/trainer.html b/dev/_modules/neuralop/training/trainer.html
index d015f2f..b164348 100644
--- a/dev/_modules/neuralop/training/trainer.html
+++ b/dev/_modules/neuralop/training/trainer.html
@@ -16,7 +16,7 @@
 
     
     
-    
+    
     
     
     
diff --git a/dev/_sources/auto_examples/checkpoint_FNO_darcy.rst.txt b/dev/_sources/auto_examples/checkpoint_FNO_darcy.rst.txt
index 10290d9..f955fbf 100644
--- a/dev/_sources/auto_examples/checkpoint_FNO_darcy.rst.txt
+++ b/dev/_sources/auto_examples/checkpoint_FNO_darcy.rst.txt
@@ -62,19 +62,12 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
 We create a tensorized FNO model
 
-.. GENERATED FROM PYTHON SOURCE LINES 35-51
+.. GENERATED FROM PYTHON SOURCE LINES 35-44
 
 .. code-block:: Python
 
 
-    model = TFNO(n_modes=(16, 16),
-                 in_channels=1, 
-                 out_channels=1, 
-                 hidden_channels=32, 
-                 projection_channels=64, 
-                 factorization='tucker', 
-                 rank=0.42)
-
+    model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
     model = model.to(device)
 
     n_params = count_model_params(model)
@@ -83,11 +76,11 @@ We create a tensorized FNO model
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 52-53
+.. GENERATED FROM PYTHON SOURCE LINES 45-46
 
 Create the optimizer
 
-.. GENERATED FROM PYTHON SOURCE LINES 53-59
+.. GENERATED FROM PYTHON SOURCE LINES 46-52
 
 .. code-block:: Python
 
@@ -98,11 +91,11 @@ Create the optimizer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 60-61
+.. GENERATED FROM PYTHON SOURCE LINES 53-54
 
 Creating the losses
 
-.. GENERATED FROM PYTHON SOURCE LINES 61-68
+.. GENERATED FROM PYTHON SOURCE LINES 54-61
 
 .. code-block:: Python
 
@@ -114,7 +107,7 @@ Creating the losses
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 69-80
+.. GENERATED FROM PYTHON SOURCE LINES 62-73
 
 .. code-block:: Python
 
@@ -130,11 +123,11 @@ Creating the losses
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 81-82
+.. GENERATED FROM PYTHON SOURCE LINES 74-75
 
 Create the trainer
 
-.. GENERATED FROM PYTHON SOURCE LINES 82-91
+.. GENERATED FROM PYTHON SOURCE LINES 75-84
 
 .. code-block:: Python
 
@@ -148,11 +141,11 @@ Create the trainer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 92-93
+.. GENERATED FROM PYTHON SOURCE LINES 85-86
 
 Actually train the model on our small Darcy-Flow dataset
 
-.. GENERATED FROM PYTHON SOURCE LINES 93-120
+.. GENERATED FROM PYTHON SOURCE LINES 86-113
 
 .. code-block:: Python
 
@@ -199,6 +192,10 @@ Actually train the model on our small Darcy-Flow dataset
 
       :download:`Download Python source code: checkpoint_FNO_darcy.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: checkpoint_FNO_darcy.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/darcy_data_processor.rst.txt b/dev/_sources/auto_examples/darcy_data_processor.rst.txt
deleted file mode 100644
index 4a6c029..0000000
--- a/dev/_sources/auto_examples/darcy_data_processor.rst.txt
+++ /dev/null
@@ -1,91 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "auto_examples/darcy_data_processor.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        :ref:`Go to the end `
-        to download the full example code.
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_auto_examples_darcy_data_processor.py:
-
-
-Data Processors
-=============================
-
-In this example, we demonstrate how to use neuralop.data.transforms.DataProcessor
-to preprocess and postprocess the small Darcy Flow example we ship with the package
-for downstream use in training a neural operator model. 
-
-.. GENERATED FROM PYTHON SOURCE LINES 12-27
-
-.. code-block:: Python
-
-    import torch
-    import matplotlib.pyplot as plt
-    import sys
-    from neuralop.models import TFNO
-    from neuralop import Trainer
-    from neuralop.training import CheckpointCallback
-    from neuralop.data.datasets import load_darcy_flow_small
-    from neuralop.utils import count_model_params
-    from neuralop import LpLoss, H1Loss
-
-    device = 'cpu'
-
-    """
-    First, let's load the small Darcy Flow dataset:
-    """
-
-.. GENERATED FROM PYTHON SOURCE LINES 28-29
-
-Loading the Navier-Stokes dataset in 128x128 resolution
-
-.. GENERATED FROM PYTHON SOURCE LINES 29-36
-
-.. code-block:: Python
-
-    train_loader, test_loaders, data_processor = load_darcy_flow_small(
-            n_train=1000, batch_size=32, 
-            test_resolutions=[16, 32], n_tests=[100, 50],
-            test_batch_sizes=[32, 32],
-            data_root="../neuralop/data/datasets/data/"
-    )
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 37-39
-
-.. code-block:: Python
-
-    """
-    Next let's visualize the data in its raw form.
-    """
-
-.. _sphx_glr_download_auto_examples_darcy_data_processor.py:
-
-.. only:: html
-
-  .. container:: sphx-glr-footer sphx-glr-footer-example
-
-    .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-      :download:`Download Jupyter notebook: darcy_data_processor.ipynb `
-
-    .. container:: sphx-glr-download sphx-glr-download-python
-
-      :download:`Download Python source code: darcy_data_processor.py `
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery `_
diff --git a/dev/_sources/auto_examples/index.rst.txt b/dev/_sources/auto_examples/index.rst.txt
index e2fa91b..6edc759 100644
--- a/dev/_sources/auto_examples/index.rst.txt
+++ b/dev/_sources/auto_examples/index.rst.txt
@@ -18,7 +18,7 @@ Gallery of examples
 
 .. raw:: html
 
-    
    
+    
    
 
 .. only:: html
 
@@ -52,7 +52,7 @@ Gallery of examples
 
 .. raw:: html
 
-    
    
+    
    
 
 .. only:: html
 
@@ -69,7 +69,7 @@ Gallery of examples
 
 .. raw:: html
 
-    
    
+    
    
 
 .. only:: html
 
@@ -80,13 +80,13 @@ Gallery of examples
 
 .. raw:: html
 
-      
    Training an FNO on Darcy-Flow
    
+      
    Training a TFNO on Darcy-Flow
    
     
    
 
 
 .. raw:: html
 
-    
    
+    
    
 
 .. only:: html
 
@@ -103,7 +103,7 @@ Gallery of examples
 
 .. raw:: html
 
-    
    
+    
    
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
index a771d21..85bebd2 100644
--- a/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
+++ b/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
@@ -18,21 +18,22 @@
 .. _sphx_glr_auto_examples_plot_FNO_darcy.py:
 
 
-Training an FNO on Darcy-Flow
+Training a TFNO on Darcy-Flow
 =============================
 
 In this example, we demonstrate how to use the small Darcy-Flow example we ship with the package
-to train a Fourier-Neural Operator
+to train a Tensorized Fourier-Neural Operator
 
-.. GENERATED FROM PYTHON SOURCE LINES 11-25
+.. GENERATED FROM PYTHON SOURCE LINES 11-26
 
 .. code-block:: Python
 
 
+
     import torch
     import matplotlib.pyplot as plt
     import sys
-    from neuralop.models import FNO
+    from neuralop.models import TFNO
     from neuralop import Trainer
     from neuralop.training import AdamW
     from neuralop.data.datasets import load_darcy_flow_small
@@ -49,11 +50,11 @@ to train a Fourier-Neural Operator
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 26-27
+.. GENERATED FROM PYTHON SOURCE LINES 27-28
 
 Loading the Navier-Stokes dataset in 128x128 resolution
 
-.. GENERATED FROM PYTHON SOURCE LINES 27-35
+.. GENERATED FROM PYTHON SOURCE LINES 28-36
 
 .. code-block:: Python
 
@@ -73,26 +74,26 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
  .. code-block:: none
 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(
     Loading test db for resolution 16 with 100 samples 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
     Loading test db for resolution 32 with 50 samples 
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 36-37
+.. GENERATED FROM PYTHON SOURCE LINES 37-38
 
-We create a simple FNO model
+We create a tensorized FNO model
 
-.. GENERATED FROM PYTHON SOURCE LINES 37-50
+.. GENERATED FROM PYTHON SOURCE LINES 38-47
 
 .. code-block:: Python
 
 
-    model = FNO(n_modes=(16, 16),
-                 in_channels=1, 
-                 out_channels=1,
-                 hidden_channels=32, 
-                 projection_channels=64)
+    model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
     model = model.to(device)
 
     n_params = count_model_params(model)
@@ -109,16 +110,16 @@ We create a simple FNO model
  .. code-block:: none
 
 
-    Our model has 1188385 parameters.
+    Our model has 523257 parameters.
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 51-52
+.. GENERATED FROM PYTHON SOURCE LINES 48-49
 
 Create the optimizer
 
-.. GENERATED FROM PYTHON SOURCE LINES 52-58
+.. GENERATED FROM PYTHON SOURCE LINES 49-55
 
 .. code-block:: Python
 
@@ -135,11 +136,11 @@ Create the optimizer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 59-60
+.. GENERATED FROM PYTHON SOURCE LINES 56-57
 
 Creating the losses
 
-.. GENERATED FROM PYTHON SOURCE LINES 60-67
+.. GENERATED FROM PYTHON SOURCE LINES 57-64
 
 .. code-block:: Python
 
@@ -157,7 +158,7 @@ Creating the losses
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 68-79
+.. GENERATED FROM PYTHON SOURCE LINES 65-76
 
 .. code-block:: Python
 
@@ -182,12 +183,12 @@ Creating the losses
 
 
     ### MODEL ###
-     FNO(
+     TFNO(
       (positional_embedding): GridEmbeddingND()
       (fno_blocks): FNOBlocks(
-        (convs): ModuleList(
-          (0-3): 4 x SpectralConv(
-            (weight): DenseTensor(shape=torch.Size([32, 32, 16, 9]), rank=None)
+        (convs): SpectralConv(
+          (weight): ModuleList(
+            (0-3): 4 x TuckerTensor(shape=(32, 32, 16, 9), rank=(26, 26, 13, 7))
           )
         )
         (fno_skips): ModuleList(
@@ -198,8 +199,8 @@ Creating the losses
       )
       (lifting): ChannelMLP(
         (fcs): ModuleList(
-          (0): Conv1d(3, 64, kernel_size=(1,), stride=(1,))
-          (1): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
+          (0): Conv1d(3, 256, kernel_size=(1,), stride=(1,))
+          (1): Conv1d(256, 32, kernel_size=(1,), stride=(1,))
         )
       )
       (projection): ChannelMLP(
@@ -222,22 +223,22 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     
+     
 
     ### LOSSES ###
 
-     * Train: 
+     * Train: 
 
-     * Test: {'h1': , 'l2': }
+     * Test: {'h1': , 'l2': }
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 80-81
+.. GENERATED FROM PYTHON SOURCE LINES 77-78
 
 Create the trainer
 
-.. GENERATED FROM PYTHON SOURCE LINES 81-90
+.. GENERATED FROM PYTHON SOURCE LINES 78-87
 
 .. code-block:: Python
 
@@ -257,11 +258,11 @@ Create the trainer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 91-92
+.. GENERATED FROM PYTHON SOURCE LINES 88-89
 
 Actually train the model on our small Darcy-Flow dataset
 
-.. GENERATED FROM PYTHON SOURCE LINES 92-102
+.. GENERATED FROM PYTHON SOURCE LINES 89-99
 
 .. code-block:: Python
 
@@ -286,26 +287,26 @@ Actually train the model on our small Darcy-Flow dataset
     Training on 1000 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([32, 1, 16, 16])
-    [0] time=1.66, avg_loss=0.5065, train_err=15.8288
-    Eval: 16_h1=0.2896, 16_l2=0.2301, 32_h1=0.3807, 32_l2=0.2436
-    [3] time=1.55, avg_loss=0.2119, train_err=6.6233
-    Eval: 16_h1=0.1865, 16_l2=0.1367, 32_h1=0.3028, 32_l2=0.1624
-    [6] time=1.54, avg_loss=0.1922, train_err=6.0064
-    Eval: 16_h1=0.2095, 16_l2=0.1634, 32_h1=0.3203, 32_l2=0.1882
-    [9] time=1.54, avg_loss=0.1814, train_err=5.6678
-    Eval: 16_h1=0.1942, 16_l2=0.1419, 32_h1=0.3132, 32_l2=0.1671
-    [12] time=1.54, avg_loss=0.1636, train_err=5.1129
-    Eval: 16_h1=0.2336, 16_l2=0.1748, 32_h1=0.3897, 32_l2=0.2129
-    [15] time=1.53, avg_loss=0.1126, train_err=3.5185
-    Eval: 16_h1=0.1868, 16_l2=0.1356, 32_h1=0.3154, 32_l2=0.1651
-    [18] time=1.54, avg_loss=0.1192, train_err=3.7263
-    Eval: 16_h1=0.1858, 16_l2=0.1359, 32_h1=0.3326, 32_l2=0.1827
+    [0] time=2.66, avg_loss=0.9003, train_err=28.1341
+    Eval: 16_h1=0.4747, 16_l2=0.3676, 32_h1=0.5249, 32_l2=0.3596
+    [3] time=2.69, avg_loss=0.3368, train_err=10.5251
+    Eval: 16_h1=0.2390, 16_l2=0.1887, 32_h1=0.3212, 32_l2=0.1959
+    [6] time=2.63, avg_loss=0.2176, train_err=6.8002
+    Eval: 16_h1=0.3172, 16_l2=0.2739, 32_h1=0.3649, 32_l2=0.2818
+    [9] time=2.66, avg_loss=0.1803, train_err=5.6347
+    Eval: 16_h1=0.1602, 16_l2=0.1168, 32_h1=0.2624, 32_l2=0.1403
+    [12] time=2.69, avg_loss=0.1738, train_err=5.4326
+    Eval: 16_h1=0.1735, 16_l2=0.1389, 32_h1=0.2747, 32_l2=0.1699
+    [15] time=2.65, avg_loss=0.1604, train_err=5.0136
+    Eval: 16_h1=0.1394, 16_l2=0.0983, 32_h1=0.2542, 32_l2=0.1292
+    [18] time=2.65, avg_loss=0.1506, train_err=4.7071
+    Eval: 16_h1=0.1596, 16_l2=0.1228, 32_h1=0.2629, 32_l2=0.1533
 
-    {'train_err': 3.398870412260294, 'avg_loss': 0.10876385319232941, 'avg_lasso_loss': None, 'epoch_train_time': 1.5358347818255424}
+    {'train_err': 4.956099387258291, 'avg_loss': 0.15859518039226533, 'avg_lasso_loss': None, 'epoch_train_time': 2.642554324999992}
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 103-113
+.. GENERATED FROM PYTHON SOURCE LINES 100-110
 
 Plot the prediction, and compare with the ground-truth 
 Note that we trained on a very small resolution for
@@ -318,7 +319,7 @@ ii) can be trained quickly on CPU
 
 In practice we would train a Neural Operator on one or multiple GPUs
 
-.. GENERATED FROM PYTHON SOURCE LINES 113-151
+.. GENERATED FROM PYTHON SOURCE LINES 110-148
 
 .. code-block:: Python
 
@@ -375,7 +376,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 32.144 seconds)
+   **Total running time of the script:** (0 minutes 54.415 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_FNO_darcy.py:
@@ -392,6 +393,10 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
       :download:`Download Python source code: plot_FNO_darcy.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_FNO_darcy.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt b/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
index e32be99..d0d3a0d 100644
--- a/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
+++ b/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
@@ -80,17 +80,12 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
 We create a tensorized FNO model
 
-.. GENERATED FROM PYTHON SOURCE LINES 33-47
+.. GENERATED FROM PYTHON SOURCE LINES 33-42
 
 .. code-block:: Python
 
 
-    model = SFNO(n_modes=(32, 32),
-                 in_channels=3,
-                 out_channels=3,
-                 hidden_channels=32,
-                 projection_channels=64,
-                 factorization='dense')
+    model = SFNO(n_modes=(32, 32), in_channels=3, out_channels=3, hidden_channels=32, projection_channels=64, factorization='dense')
     model = model.to(device)
 
     n_params = count_model_params(model)
@@ -106,21 +101,17 @@ We create a tensorized FNO model
 
  .. code-block:: none
 
-    self.sht_grids=['equiangular', 'equiangular']
-    self.sht_grids=['equiangular', 'equiangular']
-    self.sht_grids=['equiangular', 'equiangular']
-    self.sht_grids=['equiangular', 'equiangular']
 
-    Our model has 275555 parameters.
+    Our model has 278435 parameters.
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 48-49
+.. GENERATED FROM PYTHON SOURCE LINES 43-44
 
 Create the optimizer
 
-.. GENERATED FROM PYTHON SOURCE LINES 49-55
+.. GENERATED FROM PYTHON SOURCE LINES 44-50
 
 .. code-block:: Python
 
@@ -137,11 +128,11 @@ Create the optimizer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 56-57
+.. GENERATED FROM PYTHON SOURCE LINES 51-52
 
 Creating the losses
 
-.. GENERATED FROM PYTHON SOURCE LINES 57-64
+.. GENERATED FROM PYTHON SOURCE LINES 52-59
 
 .. code-block:: Python
 
@@ -159,7 +150,7 @@ Creating the losses
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 65-76
+.. GENERATED FROM PYTHON SOURCE LINES 60-71
 
 .. code-block:: Python
 
@@ -187,13 +178,13 @@ Creating the losses
      SFNO(
       (positional_embedding): GridEmbeddingND()
       (fno_blocks): FNOBlocks(
-        (convs): ModuleList(
-          (0-3): 4 x SphericalConv(
-            (weight): ComplexDenseTensor(shape=torch.Size([32, 32, 32]), rank=None)
-            (sht_handle): SHT(
-              (_SHT_cache): ModuleDict()
-              (_iSHT_cache): ModuleDict()
-            )
+        (convs): SphericalConv(
+          (weight): ModuleList(
+            (0-3): 4 x ComplexDenseTensor(shape=torch.Size([32, 32, 32]), rank=None)
+          )
+          (sht_handle): SHT(
+            (_SHT_cache): ModuleDict()
+            (_iSHT_cache): ModuleDict()
           )
         )
         (fno_skips): ModuleList(
@@ -201,22 +192,11 @@ Creating the losses
             (conv): Conv1d(32, 32, kernel_size=(1,), stride=(1,), bias=False)
           )
         )
-        (channel_mlp): ModuleList(
-          (0-3): 4 x ChannelMLP(
-            (fcs): ModuleList(
-              (0): Conv1d(32, 16, kernel_size=(1,), stride=(1,))
-              (1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))
-            )
-          )
-        )
-        (channel_mlp_skips): ModuleList(
-          (0-3): 4 x SoftGating()
-        )
       )
       (lifting): ChannelMLP(
         (fcs): ModuleList(
-          (0): Conv1d(5, 64, kernel_size=(1,), stride=(1,))
-          (1): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
+          (0): Conv1d(5, 256, kernel_size=(1,), stride=(1,))
+          (1): Conv1d(256, 32, kernel_size=(1,), stride=(1,))
         )
       )
       (projection): ChannelMLP(
@@ -239,22 +219,22 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     
+     
 
     ### LOSSES ###
 
-     * Train: 
+     * Train: 
 
-     * Test: {'l2': }
+     * Test: {'l2': }
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 77-78
+.. GENERATED FROM PYTHON SOURCE LINES 72-73
 
 Create the trainer
 
-.. GENERATED FROM PYTHON SOURCE LINES 78-86
+.. GENERATED FROM PYTHON SOURCE LINES 73-81
 
 .. code-block:: Python
 
@@ -273,11 +253,11 @@ Create the trainer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 87-88
+.. GENERATED FROM PYTHON SOURCE LINES 82-83
 
 Actually train the model on our small Darcy-Flow dataset
 
-.. GENERATED FROM PYTHON SOURCE LINES 88-98
+.. GENERATED FROM PYTHON SOURCE LINES 83-93
 
 .. code-block:: Python
 
@@ -302,26 +282,26 @@ Actually train the model on our small Darcy-Flow dataset
     Training on 200 samples
     Testing on [50, 50] samples         on resolutions [(32, 64), (64, 128)].
     Raw outputs of shape torch.Size([4, 3, 32, 64])
-    [0] time=0.91, avg_loss=2.6421, train_err=10.5682
-    Eval: (32, 64)_l2=2.1051, (64, 128)_l2=2.4542
-    [3] time=0.84, avg_loss=0.3812, train_err=1.5250
-    Eval: (32, 64)_l2=0.5328, (64, 128)_l2=2.3434
-    [6] time=0.73, avg_loss=0.2580, train_err=1.0322
-    Eval: (32, 64)_l2=0.5245, (64, 128)_l2=2.2983
-    [9] time=0.73, avg_loss=0.2163, train_err=0.8652
-    Eval: (32, 64)_l2=0.4739, (64, 128)_l2=2.2781
-    [12] time=0.73, avg_loss=0.1938, train_err=0.7753
-    Eval: (32, 64)_l2=0.4735, (64, 128)_l2=2.2642
-    [15] time=0.73, avg_loss=0.1628, train_err=0.6512
-    Eval: (32, 64)_l2=0.4826, (64, 128)_l2=2.2505
-    [18] time=0.73, avg_loss=0.1391, train_err=0.5565
-    Eval: (32, 64)_l2=0.4369, (64, 128)_l2=2.2668
+    [0] time=3.46, avg_loss=2.2655, train_err=9.0619
+    Eval: (32, 64)_l2=1.6165, (64, 128)_l2=2.6751
+    [3] time=3.47, avg_loss=0.4604, train_err=1.8414
+    Eval: (32, 64)_l2=0.5567, (64, 128)_l2=2.3834
+    [6] time=3.44, avg_loss=0.3189, train_err=1.2755
+    Eval: (32, 64)_l2=0.4640, (64, 128)_l2=2.3548
+    [9] time=3.41, avg_loss=0.2569, train_err=1.0278
+    Eval: (32, 64)_l2=0.4651, (64, 128)_l2=2.3221
+    [12] time=3.41, avg_loss=0.2121, train_err=0.8484
+    Eval: (32, 64)_l2=0.3232, (64, 128)_l2=2.3157
+    [15] time=3.40, avg_loss=0.1598, train_err=0.6390
+    Eval: (32, 64)_l2=0.2673, (64, 128)_l2=2.3022
+    [18] time=3.44, avg_loss=0.1432, train_err=0.5729
+    Eval: (32, 64)_l2=0.2376, (64, 128)_l2=2.3091
 
-    {'train_err': 0.5427721077203751, 'avg_loss': 0.13569302693009377, 'avg_lasso_loss': None, 'epoch_train_time': 0.7358115278184414}
+    {'train_err': 0.550230712890625, 'avg_loss': 0.13755767822265624, 'avg_lasso_loss': None, 'epoch_train_time': 3.4114261440000746}
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 99-109
+.. GENERATED FROM PYTHON SOURCE LINES 94-104
 
 Plot the prediction, and compare with the ground-truth 
 Note that we trained on a very small resolution for
@@ -334,7 +314,7 @@ ii) can be trained quickly on CPU
 
 In practice we would train a Neural Operator on one or multiple GPUs
 
-.. GENERATED FROM PYTHON SOURCE LINES 109-144
+.. GENERATED FROM PYTHON SOURCE LINES 104-139
 
 .. code-block:: Python
 
@@ -388,7 +368,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 20.521 seconds)
+   **Total running time of the script:** (1 minutes 25.030 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_SFNO_swe.py:
@@ -405,6 +385,10 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
       :download:`Download Python source code: plot_SFNO_swe.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_SFNO_swe.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
index e25dfa5..e6793e3 100644
--- a/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
+++ b/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
@@ -54,7 +54,7 @@ the small Darcy-Flow example we ship with the package
 
 Loading the Darcy Flow dataset
 
-.. GENERATED FROM PYTHON SOURCE LINES 28-53
+.. GENERATED FROM PYTHON SOURCE LINES 28-46
 
 .. code-block:: Python
 
@@ -64,18 +64,11 @@ Loading the Darcy Flow dataset
             test_batch_sizes=[32, 32],
     )
 
-    model = UNO(in_channels=1, 
-                out_channels=1, 
-                hidden_channels=64, 
-                projection_channels=64,
-                uno_out_channels=[32,64,64,64,32],
-                uno_n_modes=[[16,16],[8,8],[8,8],[8,8],[16,16]],
-                uno_scalings=[[1.0,1.0],[0.5,0.5],[1,1],[2,2],[1,1]],
-                horizontal_skips_map=None,
-                channel_mlp_skip="linear",
-                n_layers = 5,
-                domain_padding=0.2)
 
+
+    model = UNO(in_channels=1, out_channels=1, hidden_channels=64, projection_channels=64,uno_out_channels = [32,64,64,64,32], \
+                uno_n_modes= [[16,16],[8,8],[8,8],[8,8],[16,16]], uno_scalings=  [[1.0,1.0],[0.5,0.5],[1,1],[2,2],[1,1]],\
+                horizontal_skips_map = None, n_layers = 5, domain_padding = 0.2)
     model = model.to(device)
 
     n_params = count_model_params(model)
@@ -91,29 +84,23 @@ Loading the Darcy Flow dataset
 
  .. code-block:: none
 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(
     Loading test db for resolution 16 with 100 samples 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
     Loading test db for resolution 32 with 50 samples 
-    fno_skip='linear'
-    channel_mlp_skip='linear'
-    fno_skip='linear'
-    channel_mlp_skip='linear'
-    fno_skip='linear'
-    channel_mlp_skip='linear'
-    fno_skip='linear'
-    channel_mlp_skip='linear'
-    fno_skip='linear'
-    channel_mlp_skip='linear'
 
-    Our model has 2700097 parameters.
+    Our model has 2665921 parameters.
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 54-55
+.. GENERATED FROM PYTHON SOURCE LINES 47-48
 
 Create the optimizer
 
-.. GENERATED FROM PYTHON SOURCE LINES 55-61
+.. GENERATED FROM PYTHON SOURCE LINES 48-54
 
 .. code-block:: Python
 
@@ -130,11 +117,11 @@ Create the optimizer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 62-63
+.. GENERATED FROM PYTHON SOURCE LINES 55-56
 
 Creating the losses
 
-.. GENERATED FROM PYTHON SOURCE LINES 63-70
+.. GENERATED FROM PYTHON SOURCE LINES 56-63
 
 .. code-block:: Python
 
@@ -152,7 +139,7 @@ Creating the losses
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 71-82
+.. GENERATED FROM PYTHON SOURCE LINES 64-75
 
 .. code-block:: Python
 
@@ -188,9 +175,9 @@ Creating the losses
       )
       (fno_blocks): ModuleList(
         (0): FNOBlocks(
-          (convs): ModuleList(
-            (0): SpectralConv(
-              (weight): DenseTensor(shape=torch.Size([64, 32, 16, 9]), rank=None)
+          (convs): SpectralConv(
+            (weight): ModuleList(
+              (0): DenseTensor(shape=torch.Size([64, 32, 16, 9]), rank=None)
             )
           )
           (fno_skips): ModuleList(
@@ -198,24 +185,11 @@ Creating the losses
               (conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,), bias=False)
             )
           )
-          (channel_mlp): ModuleList(
-            (0): ChannelMLP(
-              (fcs): ModuleList(
-                (0): Conv1d(32, 16, kernel_size=(1,), stride=(1,))
-                (1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))
-              )
-            )
-          )
-          (channel_mlp_skips): ModuleList(
-            (0): Flattened1dConv(
-              (conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,), bias=False)
-            )
-          )
         )
         (1): FNOBlocks(
-          (convs): ModuleList(
-            (0): SpectralConv(
-              (weight): DenseTensor(shape=torch.Size([32, 64, 8, 5]), rank=None)
+          (convs): SpectralConv(
+            (weight): ModuleList(
+              (0): DenseTensor(shape=torch.Size([32, 64, 8, 5]), rank=None)
             )
           )
           (fno_skips): ModuleList(
@@ -223,24 +197,11 @@ Creating the losses
               (conv): Conv1d(32, 64, kernel_size=(1,), stride=(1,), bias=False)
             )
           )
-          (channel_mlp): ModuleList(
-            (0): ChannelMLP(
-              (fcs): ModuleList(
-                (0): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
-                (1): Conv1d(32, 64, kernel_size=(1,), stride=(1,))
-              )
-            )
-          )
-          (channel_mlp_skips): ModuleList(
-            (0): Flattened1dConv(
-              (conv): Conv1d(32, 64, kernel_size=(1,), stride=(1,), bias=False)
-            )
-          )
         )
         (2): FNOBlocks(
-          (convs): ModuleList(
-            (0): SpectralConv(
-              (weight): DenseTensor(shape=torch.Size([64, 64, 8, 5]), rank=None)
+          (convs): SpectralConv(
+            (weight): ModuleList(
+              (0): DenseTensor(shape=torch.Size([64, 64, 8, 5]), rank=None)
             )
           )
           (fno_skips): ModuleList(
@@ -248,24 +209,11 @@ Creating the losses
               (conv): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
             )
           )
-          (channel_mlp): ModuleList(
-            (0): ChannelMLP(
-              (fcs): ModuleList(
-                (0): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
-                (1): Conv1d(32, 64, kernel_size=(1,), stride=(1,))
-              )
-            )
-          )
-          (channel_mlp_skips): ModuleList(
-            (0): Flattened1dConv(
-              (conv): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
-            )
-          )
         )
         (3): FNOBlocks(
-          (convs): ModuleList(
-            (0): SpectralConv(
-              (weight): DenseTensor(shape=torch.Size([128, 64, 8, 5]), rank=None)
+          (convs): SpectralConv(
+            (weight): ModuleList(
+              (0): DenseTensor(shape=torch.Size([128, 64, 8, 5]), rank=None)
             )
           )
           (fno_skips): ModuleList(
@@ -273,24 +221,11 @@ Creating the losses
               (conv): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
             )
           )
-          (channel_mlp): ModuleList(
-            (0): ChannelMLP(
-              (fcs): ModuleList(
-                (0): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
-                (1): Conv1d(32, 64, kernel_size=(1,), stride=(1,))
-              )
-            )
-          )
-          (channel_mlp_skips): ModuleList(
-            (0): Flattened1dConv(
-              (conv): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
-            )
-          )
         )
         (4): FNOBlocks(
-          (convs): ModuleList(
-            (0): SpectralConv(
-              (weight): DenseTensor(shape=torch.Size([96, 32, 16, 9]), rank=None)
+          (convs): SpectralConv(
+            (weight): ModuleList(
+              (0): DenseTensor(shape=torch.Size([96, 32, 16, 9]), rank=None)
             )
           )
           (fno_skips): ModuleList(
@@ -298,19 +233,6 @@ Creating the losses
               (conv): Conv1d(96, 32, kernel_size=(1,), stride=(1,), bias=False)
             )
           )
-          (channel_mlp): ModuleList(
-            (0): ChannelMLP(
-              (fcs): ModuleList(
-                (0): Conv1d(32, 16, kernel_size=(1,), stride=(1,))
-                (1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))
-              )
-            )
-          )
-          (channel_mlp_skips): ModuleList(
-            (0): Flattened1dConv(
-              (conv): Conv1d(96, 32, kernel_size=(1,), stride=(1,), bias=False)
-            )
-          )
         )
       )
       (horizontal_skips): ModuleDict(
@@ -341,22 +263,22 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     
+     
 
     ### LOSSES ###
 
-     * Train: 
+     * Train: 
 
-     * Test: {'h1': , 'l2': }
+     * Test: {'h1': , 'l2': }
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 83-84
+.. GENERATED FROM PYTHON SOURCE LINES 76-77
 
 Create the trainer
 
-.. GENERATED FROM PYTHON SOURCE LINES 84-94
+.. GENERATED FROM PYTHON SOURCE LINES 77-87
 
 .. code-block:: Python
 
@@ -377,11 +299,11 @@ Create the trainer
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 95-96
+.. GENERATED FROM PYTHON SOURCE LINES 88-89
 
 Actually train the model on our small Darcy-Flow dataset
 
-.. GENERATED FROM PYTHON SOURCE LINES 96-106
+.. GENERATED FROM PYTHON SOURCE LINES 89-99
 
 .. code-block:: Python
 
@@ -406,26 +328,26 @@ Actually train the model on our small Darcy-Flow dataset
     Training on 1000 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([32, 1, 16, 16])
-    [0] time=4.61, avg_loss=0.6586, train_err=20.5798
-    Eval: 16_h1=0.4974, 16_l2=0.4004, 32_h1=0.8209, 32_l2=0.6873
-    [3] time=4.63, avg_loss=0.2522, train_err=7.8821
-    Eval: 16_h1=0.2281, 16_l2=0.1702, 32_h1=0.7338, 32_l2=0.5956
-    [6] time=4.52, avg_loss=0.2534, train_err=7.9194
-    Eval: 16_h1=0.2041, 16_l2=0.1527, 32_h1=0.6873, 32_l2=0.5407
-    [9] time=4.44, avg_loss=0.2240, train_err=6.9998
-    Eval: 16_h1=0.2075, 16_l2=0.1543, 32_h1=0.6566, 32_l2=0.4983
-    [12] time=4.35, avg_loss=0.2071, train_err=6.4715
-    Eval: 16_h1=0.1967, 16_l2=0.1384, 32_h1=0.6875, 32_l2=0.5304
-    [15] time=4.36, avg_loss=0.1711, train_err=5.3483
-    Eval: 16_h1=0.2300, 16_l2=0.1629, 32_h1=0.6453, 32_l2=0.4576
-    [18] time=4.43, avg_loss=0.1268, train_err=3.9616
-    Eval: 16_h1=0.1860, 16_l2=0.1351, 32_h1=0.6503, 32_l2=0.4821
+    [0] time=6.61, avg_loss=0.5430, train_err=16.9693
+    Eval: 16_h1=0.3210, 16_l2=0.2597, 32_h1=0.7922, 32_l2=0.5999
+    [3] time=6.48, avg_loss=0.2685, train_err=8.3897
+    Eval: 16_h1=0.2068, 16_l2=0.1545, 32_h1=0.7909, 32_l2=0.6063
+    [6] time=6.47, avg_loss=0.2368, train_err=7.4001
+    Eval: 16_h1=0.2155, 16_l2=0.1649, 32_h1=0.7626, 32_l2=0.5989
+    [9] time=6.47, avg_loss=0.2172, train_err=6.7885
+    Eval: 16_h1=0.2323, 16_l2=0.1762, 32_h1=0.7456, 32_l2=0.5858
+    [12] time=6.48, avg_loss=0.2048, train_err=6.3987
+    Eval: 16_h1=0.1903, 16_l2=0.1337, 32_h1=0.7315, 32_l2=0.5729
+    [15] time=6.46, avg_loss=0.1886, train_err=5.8936
+    Eval: 16_h1=0.1910, 16_l2=0.1350, 32_h1=0.7238, 32_l2=0.5652
+    [18] time=6.48, avg_loss=0.1804, train_err=5.6383
+    Eval: 16_h1=0.1816, 16_l2=0.1254, 32_h1=0.7171, 32_l2=0.5568
 
-    {'train_err': 3.9999673664569855, 'avg_loss': 0.12799895572662354, 'avg_lasso_loss': None, 'epoch_train_time': 4.439005568623543}
+    {'train_err': 5.1316939406096935, 'avg_loss': 0.16421420609951018, 'avg_lasso_loss': None, 'epoch_train_time': 6.497358972000029}
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 107-117
+.. GENERATED FROM PYTHON SOURCE LINES 100-110
 
 Plot the prediction, and compare with the ground-truth 
 Note that we trained on a very small resolution for
@@ -438,7 +360,7 @@ ii) can be trained quickly on CPU
 
 In practice we would train a Neural Operator on one or multiple GPUs
 
-.. GENERATED FROM PYTHON SOURCE LINES 117-155
+.. GENERATED FROM PYTHON SOURCE LINES 110-148
 
 .. code-block:: Python
 
@@ -495,7 +417,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 33.254 seconds)
+   **Total running time of the script:** (2 minutes 12.491 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_UNO_darcy.py:
@@ -512,6 +434,10 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
       :download:`Download Python source code: plot_UNO_darcy.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_UNO_darcy.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/plot_count_flops.rst.txt b/dev/_sources/auto_examples/plot_count_flops.rst.txt
index 3294fd1..0598e81 100644
--- a/dev/_sources/auto_examples/plot_count_flops.rst.txt
+++ b/dev/_sources/auto_examples/plot_count_flops.rst.txt
@@ -39,13 +39,13 @@ We will use the FLOP computation to compare the resources used by a base FNO.
     device = 'cpu'
 
     fno = FNO(n_modes=(64,64), 
-              in_channels=1, 
+              in_channels=3, 
               out_channels=1, 
               hidden_channels=64, 
               projection_channels=64)
 
     batch_size = 4
-    model_input = torch.randn(batch_size, 1, 128, 128)
+    model_input = torch.randn(batch_size, 3, 128, 128)
 
 
     with FlopTensorDispatchMode(fno) as ftdm:
@@ -80,7 +80,7 @@ This output is organized as a defaultdict object that counts the FLOPS used in e
 
  .. code-block:: none
 
-    defaultdict(. at 0x7f03bbb1be20>, {'': defaultdict(, {'convolution.default': 2470445056, 'bmm.default': 138412032}), 'lifting': defaultdict(, {'convolution.default': 1124073472}), 'lifting.fcs.0': defaultdict(, {'convolution.default': 50331648}), 'lifting.fcs.1': defaultdict(, {'convolution.default': 1073741824}), 'fno_blocks': defaultdict(, {'convolution.default': 1073741824, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs': defaultdict(, {'bmm.default': 138412032}), 'fno_blocks.fno_skips.1': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(, {'convolution.default': 268435456}), 'projection': defaultdict(, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(, {'convolution.default': 4194304})})
+    defaultdict(. at 0x7feab52b2e50>, {'': defaultdict(, {'convolution.default': 2503999488, 'bmm.default': 138412032}), 'lifting': defaultdict(, {'convolution.default': 1157627904}), 'lifting.fcs.0': defaultdict(, {'convolution.default': 83886080}), 'lifting.fcs.1': defaultdict(, {'convolution.default': 1073741824}), 'fno_blocks': defaultdict(, {'convolution.default': 1073741824, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs': defaultdict(, {'bmm.default': 138412032}), 'fno_blocks.fno_skips.1': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(, {'convolution.default': 268435456}), 'projection': defaultdict(, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(, {'convolution.default': 4194304})})
 
 
 
@@ -116,8 +116,8 @@ To check the maximum FLOPS used during the forward pass, let's create a recursiv
 
  .. code-block:: none
 
-    Max FLOPS required for FNO.forward: 2470445056
-    Max FLOPS required for FNO.backward: 4890558464
+    Max FLOPS required for FNO.forward: 2503999488
+    Max FLOPS required for FNO.backward: 4924112896
 
 
 
@@ -125,7 +125,7 @@ To check the maximum FLOPS used during the forward pass, let's create a recursiv
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.302 seconds)
+   **Total running time of the script:** (0 minutes 4.065 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_count_flops.py:
@@ -142,6 +142,10 @@ To check the maximum FLOPS used during the forward pass, let's create a recursiv
 
       :download:`Download Python source code: plot_count_flops.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_count_flops.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/plot_darcy_flow.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
index 55194cc..7abc160 100644
--- a/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
+++ b/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
@@ -71,7 +71,11 @@ Training samples are 16x16 and we load testing samples at both
 
  .. code-block:: none
 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(
     Loading test db for resolution 16 with 50 samples 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
     Loading test db for resolution 32 with 50 samples 
 
 
@@ -159,7 +163,7 @@ Visualizing the data
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.186 seconds)
+   **Total running time of the script:** (0 minutes 0.429 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow.py:
@@ -176,6 +180,10 @@ Visualizing the data
 
       :download:`Download Python source code: plot_darcy_flow.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_darcy_flow.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
index dcb6c9c..72d9c2d 100644
--- a/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
+++ b/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
@@ -149,7 +149,11 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
  .. code-block:: none
 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(
     Loading test db for resolution 16 with 50 samples 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
     Original dataset shape torch.Size([50, 1, 16, 16])
 
 
@@ -206,7 +210,7 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
  .. code-block:: none
 
-    /home/dave/myneurop/examples/plot_darcy_flow_spectrum.py:104: UserWarning: Attempt to set non-positive ylim on a log-scaled axis will be ignored.
+    /home/runner/work/neuraloperator/neuraloperator/examples/plot_darcy_flow_spectrum.py:104: UserWarning: Attempt to set non-positive ylim on a log-scaled axis will be ignored.
       ax.set_ylim(10, 10^10)
 
 
@@ -215,7 +219,7 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.139 seconds)
+   **Total running time of the script:** (0 minutes 0.279 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow_spectrum.py:
@@ -232,6 +236,10 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
       :download:`Download Python source code: plot_darcy_flow_spectrum.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_darcy_flow_spectrum.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt
index 2076ac9..b7f63db 100644
--- a/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt
+++ b/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt
@@ -70,7 +70,11 @@ Loading the Darcy flow dataset
 
  .. code-block:: none
 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(
     Loading test db for resolution 16 with 100 samples 
+    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+      data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
     Loading test db for resolution 32 with 50 samples 
 
 
@@ -220,7 +224,7 @@ Set up the losses
 
 
     ### N PARAMS ###
-     2110305
+     2119329
 
     ### OPTIMIZER ###
      AdamW (
@@ -234,15 +238,15 @@ Set up the losses
     )
 
     ### SCHEDULER ###
-     
+     
 
     ### LOSSES ###
 
     ### INCREMENTAL RESOLUTION + GRADIENT EXPLAINED ###
 
-     * Train: 
+     * Train: 
 
-     * Test: {'h1': , 'l2': }
+     * Test: {'h1': , 'l2': }
 
 
 
@@ -323,51 +327,51 @@ Train the model
     Training on 100 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([16, 1, 8, 8])
-    [0] time=0.07, avg_loss=0.8236, train_err=11.7655
-    Eval: 16_h1=0.7665, 16_l2=0.6166, 32_h1=0.7932, 32_l2=0.6111
-    [1] time=0.03, avg_loss=0.6737, train_err=9.6245
-    Eval: 16_h1=0.6417, 16_l2=0.4694, 32_h1=0.7263, 32_l2=0.4793
-    [2] time=0.03, avg_loss=0.5479, train_err=7.8269
-    Eval: 16_h1=0.6615, 16_l2=0.4694, 32_h1=0.8183, 32_l2=0.4896
-    [3] time=0.03, avg_loss=0.5040, train_err=7.1998
-    Eval: 16_h1=0.5634, 16_l2=0.4002, 32_h1=0.6886, 32_l2=0.4146
-    [4] time=0.04, avg_loss=0.4482, train_err=6.4026
-    Eval: 16_h1=0.5917, 16_l2=0.4267, 32_h1=0.7822, 32_l2=0.4703
-    [5] time=0.04, avg_loss=0.4120, train_err=5.8851
-    Eval: 16_h1=0.5466, 16_l2=0.3766, 32_h1=0.7625, 32_l2=0.4164
-    [6] time=0.04, avg_loss=0.3683, train_err=5.2609
-    Eval: 16_h1=0.4672, 16_l2=0.3235, 32_h1=0.6485, 32_l2=0.3597
-    [7] time=0.04, avg_loss=0.3326, train_err=4.7513
-    Eval: 16_h1=0.4938, 16_l2=0.3391, 32_h1=0.6948, 32_l2=0.3841
-    [8] time=0.04, avg_loss=0.3064, train_err=4.3769
-    Eval: 16_h1=0.4673, 16_l2=0.3198, 32_h1=0.6777, 32_l2=0.3651
-    [9] time=0.04, avg_loss=0.2868, train_err=4.0965
-    Eval: 16_h1=0.4539, 16_l2=0.3072, 32_h1=0.6651, 32_l2=0.3536
+    [0] time=0.21, avg_loss=0.7750, train_err=11.0714
+    Eval: 16_h1=0.7031, 16_l2=0.5348, 32_h1=0.7319, 32_l2=0.5357
+    [1] time=0.21, avg_loss=0.5908, train_err=8.4395
+    Eval: 16_h1=0.6114, 16_l2=0.4391, 32_h1=0.6716, 32_l2=0.4473
+    [2] time=0.20, avg_loss=0.5093, train_err=7.2754
+    Eval: 16_h1=0.5647, 16_l2=0.3843, 32_h1=0.6667, 32_l2=0.3946
+    [3] time=0.21, avg_loss=0.4408, train_err=6.2975
+    Eval: 16_h1=0.5216, 16_l2=0.3600, 32_h1=0.6661, 32_l2=0.3915
+    [4] time=0.21, avg_loss=0.4055, train_err=5.7927
+    Eval: 16_h1=0.5165, 16_l2=0.3631, 32_h1=0.6852, 32_l2=0.4008
+    [5] time=0.21, avg_loss=0.3794, train_err=5.4201
+    Eval: 16_h1=0.5407, 16_l2=0.4053, 32_h1=0.6456, 32_l2=0.4213
+    [6] time=0.22, avg_loss=0.3662, train_err=5.2311
+    Eval: 16_h1=0.4848, 16_l2=0.3434, 32_h1=0.6641, 32_l2=0.3786
+    [7] time=0.22, avg_loss=0.3320, train_err=4.7433
+    Eval: 16_h1=0.4515, 16_l2=0.3280, 32_h1=0.5890, 32_l2=0.3661
+    [8] time=0.22, avg_loss=0.3013, train_err=4.3041
+    Eval: 16_h1=0.4443, 16_l2=0.3024, 32_h1=0.6300, 32_l2=0.3467
+    [9] time=0.23, avg_loss=0.2621, train_err=3.7436
+    Eval: 16_h1=0.4252, 16_l2=0.2978, 32_h1=0.6085, 32_l2=0.3395
     Incre Res Update: change index to 1
     Incre Res Update: change sub to 1
     Incre Res Update: change res to 16
-    [10] time=0.06, avg_loss=0.3922, train_err=5.6035
-    Eval: 16_h1=0.3690, 16_l2=0.2683, 32_h1=0.4566, 32_l2=0.2656
-    [11] time=0.04, avg_loss=0.3392, train_err=4.8454
-    Eval: 16_h1=0.3419, 16_l2=0.2484, 32_h1=0.4551, 32_l2=0.2650
-    [12] time=0.04, avg_loss=0.3098, train_err=4.4253
-    Eval: 16_h1=0.3172, 16_l2=0.2389, 32_h1=0.4273, 32_l2=0.2547
-    [13] time=0.04, avg_loss=0.2827, train_err=4.0392
-    Eval: 16_h1=0.3155, 16_l2=0.2391, 32_h1=0.4182, 32_l2=0.2516
-    [14] time=0.04, avg_loss=0.2774, train_err=3.9626
-    Eval: 16_h1=0.2872, 16_l2=0.2186, 32_h1=0.3938, 32_l2=0.2344
-    [15] time=0.04, avg_loss=0.2490, train_err=3.5571
-    Eval: 16_h1=0.2744, 16_l2=0.2075, 32_h1=0.3745, 32_l2=0.2220
-    [16] time=0.04, avg_loss=0.2346, train_err=3.3511
-    Eval: 16_h1=0.2758, 16_l2=0.2056, 32_h1=0.3889, 32_l2=0.2249
-    [17] time=0.04, avg_loss=0.2293, train_err=3.2760
-    Eval: 16_h1=0.2793, 16_l2=0.2077, 32_h1=0.3904, 32_l2=0.2213
-    [18] time=0.04, avg_loss=0.2262, train_err=3.2321
-    Eval: 16_h1=0.3024, 16_l2=0.2263, 32_h1=0.4136, 32_l2=0.2417
-    [19] time=0.04, avg_loss=0.2388, train_err=3.4117
-    Eval: 16_h1=0.3040, 16_l2=0.2327, 32_h1=0.4129, 32_l2=0.2462
-
-    {'train_err': 3.411697438785008, 'avg_loss': 0.23881882071495056, 'avg_lasso_loss': None, 'epoch_train_time': 0.04143996722996235, '16_h1': tensor(0.3040, device='cuda:0'), '16_l2': tensor(0.2327, device='cuda:0'), '32_h1': tensor(0.4129, device='cuda:0'), '32_l2': tensor(0.2462, device='cuda:0')}
+    [10] time=0.30, avg_loss=0.3530, train_err=5.0422
+    Eval: 16_h1=0.3418, 16_l2=0.2496, 32_h1=0.4258, 32_l2=0.2477
+    [11] time=0.29, avg_loss=0.2891, train_err=4.1300
+    Eval: 16_h1=0.3833, 16_l2=0.2783, 32_h1=0.4696, 32_l2=0.2820
+    [12] time=0.31, avg_loss=0.2975, train_err=4.2504
+    Eval: 16_h1=0.3179, 16_l2=0.2267, 32_h1=0.4156, 32_l2=0.2404
+    [13] time=0.31, avg_loss=0.2420, train_err=3.4567
+    Eval: 16_h1=0.2829, 16_l2=0.2034, 32_h1=0.3807, 32_l2=0.2174
+    [14] time=0.31, avg_loss=0.2147, train_err=3.0676
+    Eval: 16_h1=0.3394, 16_l2=0.2630, 32_h1=0.4255, 32_l2=0.2714
+    [15] time=0.32, avg_loss=0.2232, train_err=3.1885
+    Eval: 16_h1=0.3785, 16_l2=0.2985, 32_h1=0.4668, 32_l2=0.3102
+    [16] time=0.32, avg_loss=0.2555, train_err=3.6494
+    Eval: 16_h1=0.3279, 16_l2=0.2593, 32_h1=0.4078, 32_l2=0.2623
+    [17] time=0.32, avg_loss=0.2769, train_err=3.9559
+    Eval: 16_h1=0.4073, 16_l2=0.3371, 32_h1=0.4499, 32_l2=0.3422
+    [18] time=0.32, avg_loss=0.2840, train_err=4.0576
+    Eval: 16_h1=0.2826, 16_l2=0.2202, 32_h1=0.3649, 32_l2=0.2272
+    [19] time=0.32, avg_loss=0.1984, train_err=2.8340
+    Eval: 16_h1=0.2795, 16_l2=0.2186, 32_h1=0.3582, 32_l2=0.2296
+
+    {'train_err': 2.8339713641575406, 'avg_loss': 0.19837799549102783, 'avg_lasso_loss': None, 'epoch_train_time': 0.3178346639999745, '16_h1': tensor(0.2795), '16_l2': tensor(0.2186), '32_h1': tensor(0.3582), '32_l2': tensor(0.2296)}
 
 
 
@@ -441,7 +445,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.140 seconds)
+   **Total running time of the script:** (0 minutes 7.776 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_incremental_FNO_darcy.py:
@@ -458,6 +462,10 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
       :download:`Download Python source code: plot_incremental_FNO_darcy.py `
 
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_incremental_FNO_darcy.zip `
+
 
 .. only:: html
 
diff --git a/dev/_sources/auto_examples/sg_execution_times.rst.txt b/dev/_sources/auto_examples/sg_execution_times.rst.txt
index 7f3a427..10a5c1f 100644
--- a/dev/_sources/auto_examples/sg_execution_times.rst.txt
+++ b/dev/_sources/auto_examples/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**01:33.254** total execution time for 8 files **from auto_examples**:
+**04:44.485** total execution time for 8 files **from auto_examples**:
 
 .. container::
 
@@ -33,26 +33,26 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_UNO_darcy.py` (``plot_UNO_darcy.py``)
-     - 01:33.254
+     - 02:12.491
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_checkpoint_FNO_darcy.py` (``checkpoint_FNO_darcy.py``)
-     - 00:00.000
+   * - :ref:`sphx_glr_auto_examples_plot_SFNO_swe.py` (``plot_SFNO_swe.py``)
+     - 01:25.030
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_FNO_darcy.py` (``plot_FNO_darcy.py``)
-     - 00:00.000
+     - 00:54.415
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_SFNO_swe.py` (``plot_SFNO_swe.py``)
-     - 00:00.000
+   * - :ref:`sphx_glr_auto_examples_plot_incremental_FNO_darcy.py` (``plot_incremental_FNO_darcy.py``)
+     - 00:07.776
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_count_flops.py` (``plot_count_flops.py``)
-     - 00:00.000
+     - 00:04.065
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_darcy_flow.py` (``plot_darcy_flow.py``)
-     - 00:00.000
+     - 00:00.429
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_darcy_flow_spectrum.py` (``plot_darcy_flow_spectrum.py``)
-     - 00:00.000
+     - 00:00.279
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_incremental_FNO_darcy.py` (``plot_incremental_FNO_darcy.py``)
+   * - :ref:`sphx_glr_auto_examples_checkpoint_FNO_darcy.py` (``checkpoint_FNO_darcy.py``)
      - 00:00.000
      - 0.0
diff --git a/dev/_sources/modules/_autosummary/neuralop.models.FNO.rst.txt b/dev/_sources/modules/_autosummary/neuralop.models.FNO.rst.txt
deleted file mode 100644
index 6c30201..0000000
--- a/dev/_sources/modules/_autosummary/neuralop.models.FNO.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.models`.FNO
-=================================
-
-.. currentmodule:: neuralop.models
-
-.. autoclass:: FNO
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/api.rst.txt b/dev/_sources/modules/api.rst.txt
index 3521ca1..ec19ae7 100644
--- a/dev/_sources/modules/api.rst.txt
+++ b/dev/_sources/modules/api.rst.txt
@@ -111,7 +111,7 @@ In addition to the full architectures, we also provide
 in :mod:`neuralop.layers` building blocks,
 in the form of PyTorch layers, that you can use to build your own models:
 
-Neural operator layers
+Neural operator Layers
 ------------------------
 
 **Spectral convolutions** (in Fourier domain):
@@ -170,24 +170,6 @@ To support geometry-informed (GINO) models, we also offer the ability to integra
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-**Embeddings**
-
-Apply positional embeddings as additional channels on a function:
-
-.. automodule:: neuralop.layers.embeddings
-    :no-members:
-    :no-inherited-members:
-
-.. autosummary::
-    :toctree: generated
-    :template: class.rst
-
-    GridEmbeddingND
-    GridEmbedding2D
-    SinusoidalEmbedding2D
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
 **Neighbor search**
 
 Find neighborhoods on arbitrary coordinate meshes:
@@ -210,7 +192,6 @@ Find neighborhoods on arbitrary coordinate meshes:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-
 Other resolution invariant operations
 -------------------------------------
 
diff --git a/dev/_sources/modules/generated/neuralop.datasets.data_transforms.DefaultDataProcessor.rst.txt b/dev/_sources/modules/generated/neuralop.datasets.data_transforms.DefaultDataProcessor.rst.txt
deleted file mode 100644
index e5de184..0000000
--- a/dev/_sources/modules/generated/neuralop.datasets.data_transforms.DefaultDataProcessor.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.datasets.data_transforms`.DefaultDataProcessor
-====================================================================
-
-.. currentmodule:: neuralop.datasets.data_transforms
-
-.. autoclass:: DefaultDataProcessor
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.datasets.data_transforms.MGPatchingDataProcessor.rst.txt b/dev/_sources/modules/generated/neuralop.datasets.data_transforms.MGPatchingDataProcessor.rst.txt
deleted file mode 100644
index 31184fe..0000000
--- a/dev/_sources/modules/generated/neuralop.datasets.data_transforms.MGPatchingDataProcessor.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.datasets.data_transforms`.MGPatchingDataProcessor
-=======================================================================
-
-.. currentmodule:: neuralop.datasets.data_transforms
-
-.. autoclass:: MGPatchingDataProcessor
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.datasets.load_darcy_flow_small.rst.txt b/dev/_sources/modules/generated/neuralop.datasets.load_darcy_flow_small.rst.txt
deleted file mode 100644
index f8d11c5..0000000
--- a/dev/_sources/modules/generated/neuralop.datasets.load_darcy_flow_small.rst.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-:mod:`neuralop.datasets`.load_darcy_flow_small
-===========================================================
-
-.. currentmodule:: neuralop.datasets
-
-.. autofunction:: load_darcy_flow_small
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.layers.embeddings.GridEmbedding2D.rst.txt b/dev/_sources/modules/generated/neuralop.layers.embeddings.GridEmbedding2D.rst.txt
deleted file mode 100644
index f92127e..0000000
--- a/dev/_sources/modules/generated/neuralop.layers.embeddings.GridEmbedding2D.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.layers.embeddings`.GridEmbedding2D
-========================================================
-
-.. currentmodule:: neuralop.layers.embeddings
-
-.. autoclass:: GridEmbedding2D
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.layers.embeddings.GridEmbeddingND.rst.txt b/dev/_sources/modules/generated/neuralop.layers.embeddings.GridEmbeddingND.rst.txt
deleted file mode 100644
index 4cdf0a7..0000000
--- a/dev/_sources/modules/generated/neuralop.layers.embeddings.GridEmbeddingND.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.layers.embeddings`.GridEmbeddingND
-========================================================
-
-.. currentmodule:: neuralop.layers.embeddings
-
-.. autoclass:: GridEmbeddingND
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.layers.embeddings.SinusoidalEmbedding2D.rst.txt b/dev/_sources/modules/generated/neuralop.layers.embeddings.SinusoidalEmbedding2D.rst.txt
deleted file mode 100644
index beb4d36..0000000
--- a/dev/_sources/modules/generated/neuralop.layers.embeddings.SinusoidalEmbedding2D.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.layers.embeddings`.SinusoidalEmbedding2D
-==============================================================
-
-.. currentmodule:: neuralop.layers.embeddings
-
-.. autoclass:: SinusoidalEmbedding2D
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.layers.gno_block.GNOBlock.rst.txt b/dev/_sources/modules/generated/neuralop.layers.gno_block.GNOBlock.rst.txt
deleted file mode 100644
index 3d47fbd..0000000
--- a/dev/_sources/modules/generated/neuralop.layers.gno_block.GNOBlock.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.layers.gno_block`.GNOBlock
-================================================
-
-.. currentmodule:: neuralop.layers.gno_block
-
-.. autoclass:: GNOBlock
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.training.callbacks.BasicLoggerCallback.rst.txt b/dev/_sources/modules/generated/neuralop.training.callbacks.BasicLoggerCallback.rst.txt
deleted file mode 100644
index 7ad12c5..0000000
--- a/dev/_sources/modules/generated/neuralop.training.callbacks.BasicLoggerCallback.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.training.callbacks`.BasicLoggerCallback
-=============================================================
-
-.. currentmodule:: neuralop.training.callbacks
-
-.. autoclass:: BasicLoggerCallback
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.training.callbacks.Callback.rst.txt b/dev/_sources/modules/generated/neuralop.training.callbacks.Callback.rst.txt
deleted file mode 100644
index 403e8d9..0000000
--- a/dev/_sources/modules/generated/neuralop.training.callbacks.Callback.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.training.callbacks`.Callback
-==================================================
-
-.. currentmodule:: neuralop.training.callbacks
-
-.. autoclass:: Callback
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/neuralop.training.callbacks.CheckpointCallback.rst.txt b/dev/_sources/modules/generated/neuralop.training.callbacks.CheckpointCallback.rst.txt
deleted file mode 100644
index 2bf7430..0000000
--- a/dev/_sources/modules/generated/neuralop.training.callbacks.CheckpointCallback.rst.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-:mod:`neuralop.training.callbacks`.CheckpointCallback
-============================================================
-
-.. currentmodule:: neuralop.training.callbacks
-
-.. autoclass:: CheckpointCallback
-   :members:
-
-.. raw:: html
-
-    
    
\ No newline at end of file
diff --git a/dev/_sources/sg_execution_times.rst.txt b/dev/_sources/sg_execution_times.rst.txt
index 9da0481..8c18dd5 100644
--- a/dev/_sources/sg_execution_times.rst.txt
+++ b/dev/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**01:33.254** total execution time for 8 files **from all galleries**:
+**04:44.485** total execution time for 8 files **from all galleries**:
 
 .. container::
 
@@ -33,26 +33,26 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_UNO_darcy.py` (``../../examples/plot_UNO_darcy.py``)
-     - 01:33.254
+     - 02:12.491
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_checkpoint_FNO_darcy.py` (``../../examples/checkpoint_FNO_darcy.py``)
-     - 00:00.000
+   * - :ref:`sphx_glr_auto_examples_plot_SFNO_swe.py` (``../../examples/plot_SFNO_swe.py``)
+     - 01:25.030
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_FNO_darcy.py` (``../../examples/plot_FNO_darcy.py``)
-     - 00:00.000
+     - 00:54.415
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_SFNO_swe.py` (``../../examples/plot_SFNO_swe.py``)
-     - 00:00.000
+   * - :ref:`sphx_glr_auto_examples_plot_incremental_FNO_darcy.py` (``../../examples/plot_incremental_FNO_darcy.py``)
+     - 00:07.776
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_count_flops.py` (``../../examples/plot_count_flops.py``)
-     - 00:00.000
+     - 00:04.065
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_darcy_flow.py` (``../../examples/plot_darcy_flow.py``)
-     - 00:00.000
+     - 00:00.429
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_darcy_flow_spectrum.py` (``../../examples/plot_darcy_flow_spectrum.py``)
-     - 00:00.000
+     - 00:00.279
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_incremental_FNO_darcy.py` (``../../examples/plot_incremental_FNO_darcy.py``)
+   * - :ref:`sphx_glr_auto_examples_checkpoint_FNO_darcy.py` (``../../examples/checkpoint_FNO_darcy.py``)
      - 00:00.000
      - 0.0
diff --git a/dev/_sources/user_guide/fno.rst.txt b/dev/_sources/user_guide/fno.rst.txt
index 3f09950..581b47e 100644
--- a/dev/_sources/user_guide/fno.rst.txt
+++ b/dev/_sources/user_guide/fno.rst.txt
@@ -1,4 +1,3 @@
-.. _fno-guide:
 ========================
 Fourier Neural Operators
 ========================
diff --git a/dev/_sources/user_guide/getting_started.rst.txt b/dev/_sources/user_guide/getting_started.rst.txt
deleted file mode 100644
index 3a24d0b..0000000
--- a/dev/_sources/user_guide/getting_started.rst.txt
+++ /dev/null
@@ -1,47 +0,0 @@
-.. _getting_started :
-
-Getting Started
-===========
-
-*NeuralOperator* aims to provide you with all the tools 
-to easily use, build and train neural operators for your own applications
-and learn mapping between function spaces, in PyTorch.
-
-Intro to operator learning
-----------------------------
-To get a better feel for the theory behind our neural operator models, see :ref:`neuralop_intro` and :ref:`fno_intro`.
-
-~~~~~~~~~~~~
-
-Interactive examples with code
-----------------------------
-To get up to speed on the code, and look through some interactive examples to help you hit the ground running,
-check out :ref:`gallery_examples`.
-
-~~~~~~~~~~~~
-
-NeuralOperator library structure
----------------------------------
-
-Here are the main components of the library:
-
-================================= ================================
-Module                             Description
-================================= ================================
-:mod:`neuralop`                   Main library 
-:mod:`neuralop.models`            Full ready-to-use neural operators
-:mod:`neuralop.layers`            Individual layers to build neural operators
-:mod:`neuralop.datasets`          Convenience PyTorch data loaders for PDE datasets
-:mod:`neuralop.training`          Utilities to train neural operators end-to-end
-================================= ================================
-
-The full API documentation is provided in :ref:`api_ref`.
-
-Finally, if you're building the library from source, your repository will also include the following directories:
-
-================================= ================================
-Directory                         Description
-================================= ================================
-:mod:`scripts`                    Training recipe scripts for our models on sample problems
-:mod:`examples`                   More documented interactive examples, seen in 
-================================= ================================
diff --git a/dev/_sources/user_guide/index_old.rst.txt b/dev/_sources/user_guide/index_old.rst.txt
deleted file mode 100644
index 2352602..0000000
--- a/dev/_sources/user_guide/index_old.rst.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-.. _user_guide:
-
-
-User guide
-==========
-
-
-.. toctree::
-
-   getting_started
-   neural_operators
-   fno
-   training
diff --git a/dev/_static/searchtools.js b/dev/_static/searchtools.js
index 92da3f8..b08d58c 100644
--- a/dev/_static/searchtools.js
+++ b/dev/_static/searchtools.js
@@ -178,7 +178,7 @@ const Search = {
 
   htmlToText: (htmlString, anchor) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    for (const removalQuery of [".headerlinks", "script", "style"]) {
+    for (const removalQuery of [".headerlink", "script", "style"]) {
       htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
     }
     if (anchor) {
@@ -328,13 +328,14 @@ const Search = {
     for (const [title, foundTitles] of Object.entries(allTitles)) {
       if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
-          let score = Math.round(100 * queryLower.length / title.length)
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
           normalResults.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
             null,
-            score,
+            score + boost,
             filenames[file],
           ]);
         }
diff --git a/dev/_static/sg_gallery.css b/dev/_static/sg_gallery.css
index 7222783..9bcd33c 100644
--- a/dev/_static/sg_gallery.css
+++ b/dev/_static/sg_gallery.css
@@ -178,23 +178,44 @@ thumbnail with its default link Background color */
   max-height: 112px;
   max-width: 160px;
 }
-.sphx-glr-thumbcontainer[tooltip]:hover:after {
-  background: var(--sg-tooltip-background);
+
+.sphx-glr-thumbcontainer[tooltip]::before {
+  content: "";
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  z-index: 97;
+  background-color: var(--sg-tooltip-background);
+  backdrop-filter: blur(3px);
+  opacity: 0;
+  transition: opacity 0.3s;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::before {
+  opacity: 1;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::after {
   -webkit-border-radius: 4px;
   -moz-border-radius: 4px;
   border-radius: 4px;
   color: var(--sg-tooltip-foreground);
   content: attr(tooltip);
-  padding: 10px;
+  padding: 10px 10px 5px;
   z-index: 98;
   width: 100%;
-  height: 100%;
+  max-height: 100%;
   position: absolute;
   pointer-events: none;
   top: 0;
   box-sizing: border-box;
   overflow: hidden;
-  backdrop-filter: blur(3px);
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-line-clamp: 6;
 }
 
 .sphx-glr-script-out {
@@ -283,6 +304,10 @@ div.sphx-glr-download a:hover {
   background-color: var(--sg-download-a-hover-background-color);
 }
 
+div.sphx-glr-sidebar-item img {
+  max-height: 20px;
+}
+
 .sphx-glr-example-title:target::before {
   display: block;
   content: "";
diff --git a/dev/auto_examples/checkpoint_FNO_darcy.html b/dev/auto_examples/checkpoint_FNO_darcy.html
index bd9e11b..7585685 100644
--- a/dev/auto_examples/checkpoint_FNO_darcy.html
+++ b/dev/auto_examples/checkpoint_FNO_darcy.html
@@ -17,7 +17,7 @@
 
     
     
-    
+    
     
     
     
@@ -31,7 +31,7 @@
  
     
     
-    
+    
     
 
 
@@ -132,7 +132,7 @@
 
  • Using torchtnt to count FLOPS
    • 
 
  • A simple Darcy-Flow dataset
    • 
 
  • Training a TFNO on Darcy-Flow
    • 
-
  • Training an FNO on Darcy-Flow
    • 
+
  • Training a TFNO on Darcy-Flow
    • 
 
  • Training a SFNO on the spherical Shallow Water equations
    • 
 
  • U-NO on Darcy-Flow
    • 
 
  • A simple Darcy-Flow spectrum analysis
    • 
@@ -194,14 +194,7 @@
 
    
 
    
 
    We create a tensorized FNO model
    
-
    model = TFNO(n_modes=(16, 16),
-             in_channels=1,
-             out_channels=1,
-             hidden_channels=32,
-             projection_channels=64,
-             factorization='tucker',
-             rank=0.42)
-
+
    model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
 model = model.to(device)
 
 n_params = count_model_params(model)
@@ -280,6 +273,9 @@
 
    
 
    Download Python source code: checkpoint_FNO_darcy.py
    
 
    
+
    
+
    Download zipped: checkpoint_FNO_darcy.zip
    
+
    
 
    
 
    Gallery generated by Sphinx-Gallery
    
 
@@ -299,7 +295,7 @@
     
     
     
-        Training an FNO on Darcy-Flow 
+        Training a TFNO on Darcy-Flow 
         
             
         
diff --git a/dev/auto_examples/darcy_data_processor.html b/dev/auto_examples/darcy_data_processor.html
deleted file mode 100644
index 566da0b..0000000
--- a/dev/auto_examples/darcy_data_processor.html
+++ /dev/null
@@ -1,255 +0,0 @@
-
-
-
-  
-    
-    
-
-    Data Processors — neuraloperator 0.3.0 documentation 
-
-
-
-
-
-
-
-
-
-    
-    
-    
-    
-    
-    
-
-  
-    
-    
-    
- 
- 
- 
-    
-    
-
-
-  
-
-
-  
    
-    
-      
-      
-    
-  
    
-
-
-  
    
-  
    
-	
-  
-      
-  
-
-  
    
-
-    
-    
    
-
-      
-      
-      
    
-        
-      
    
-      
-
-      
    
-        
-  
    
-
    Note
    
-
    Go to the end
-to download the full example code.
    
-
    
-
    
-
    Data Processors
    
-
    In this example, we demonstrate how to use neuralop.data.transforms.DataProcessor
-to preprocess and postprocess the small Darcy Flow example we ship with the package
-for downstream use in training a neural operator model.
    
-
    import torch
-import matplotlib.pyplot as plt
-import sys
-from neuralop.models import TFNO
-from neuralop import Trainer
-from neuralop.training import CheckpointCallback
-from neuralop.data.datasets import load_darcy_flow_small
-from neuralop.utils import count_model_params
-from neuralop import LpLoss, H1Loss
-
-device = 'cpu'
-
-"""
-First, let's load the small Darcy Flow dataset:
-"""
-
    
-
    
-
    Loading the Navier-Stokes dataset in 128x128 resolution
    
-
    train_loader, test_loaders, data_processor = load_darcy_flow_small(
-        n_train=1000, batch_size=32,
-        test_resolutions=[16, 32], n_tests=[100, 50],
-        test_batch_sizes=[32, 32],
-        data_root="../neuralop/data/datasets/data/"
-)
-
    
-
    
-
    """
-Next let's visualize the data in its raw form.
-"""
-
    
-
    
-
-
    Gallery generated by Sphinx-Gallery
    
-
    
-
-
-      
    
-
-      
-        
-
-      
-
-        
    
-    
    
-        
    
-          © Copyright 2024, Jean Kossaifi, David Pitt, Nikola Kovachki, Zongyi Li and Anima Anandkumar.
    
-        
    
-    
    
-  
    
-
-    
    
-
-  
      
-
-	
-    
-
-  
-
-  
      
-  
     
-
-  
-   
-
-
-
-  
-
\ No newline at end of file
diff --git a/dev/auto_examples/index.html b/dev/auto_examples/index.html
index 6a2d38a..3762738 100644
--- a/dev/auto_examples/index.html
+++ b/dev/auto_examples/index.html
@@ -17,7 +17,7 @@
 
     
     
-    
+    
     
     
     
@@ -132,7 +132,7 @@
 
  • Using torchtnt to count FLOPS
    • 
 
  • A simple Darcy-Flow dataset
    • 
 
  • Training a TFNO on Darcy-Flow
    • 
-
  • Training an FNO on Darcy-Flow
    • 
+
  • Training a TFNO on Darcy-Flow
    • 
 
  • Training a SFNO on the spherical Shallow Water equations
    • 
 
  • U-NO on Darcy-Flow
    • 
 
  • A simple Darcy-Flow spectrum analysis
    • 
@@ -165,22 +165,22 @@
         
   
    
 
    
 
    We create a tensorized FNO model
    
-
    model = SFNO(n_modes=(32, 32),
-             in_channels=3,
-             out_channels=3,
-             hidden_channels=32,
-             projection_channels=64,
-             factorization='dense')
+
    model = SFNO(n_modes=(32, 32), in_channels=3, out_channels=3, hidden_channels=32, projection_channels=64, factorization='dense')
 model = model.to(device)
 
 n_params = count_model_params(model)
@@ -209,12 +204,7 @@
 sys.stdout.flush()
 
    
 
    
-
    self.sht_grids=['equiangular', 'equiangular']
-self.sht_grids=['equiangular', 'equiangular']
-self.sht_grids=['equiangular', 'equiangular']
-self.sht_grids=['equiangular', 'equiangular']
-
-Our model has 275555 parameters.
+
    Our model has 278435 parameters.
 
    
 
    
 
    Create the optimizer
    
@@ -245,13 +235,13 @@
  SFNO(
   (positional_embedding): GridEmbeddingND()
   (fno_blocks): FNOBlocks(
-    (convs): ModuleList(
-      (0-3): 4 x SphericalConv(
-        (weight): ComplexDenseTensor(shape=torch.Size([32, 32, 32]), rank=None)
-        (sht_handle): SHT(
-          (_SHT_cache): ModuleDict()
-          (_iSHT_cache): ModuleDict()
-        )
+    (convs): SphericalConv(
+      (weight): ModuleList(
+        (0-3): 4 x ComplexDenseTensor(shape=torch.Size([32, 32, 32]), rank=None)
+      )
+      (sht_handle): SHT(
+        (_SHT_cache): ModuleDict()
+        (_iSHT_cache): ModuleDict()
       )
     )
     (fno_skips): ModuleList(
@@ -259,22 +249,11 @@
         (conv): Conv1d(32, 32, kernel_size=(1,), stride=(1,), bias=False)
       )
     )
-    (channel_mlp): ModuleList(
-      (0-3): 4 x ChannelMLP(
-        (fcs): ModuleList(
-          (0): Conv1d(32, 16, kernel_size=(1,), stride=(1,))
-          (1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))
-        )
-      )
-    )
-    (channel_mlp_skips): ModuleList(
-      (0-3): 4 x SoftGating()
-    )
   )
   (lifting): ChannelMLP(
     (fcs): ModuleList(
-      (0): Conv1d(5, 64, kernel_size=(1,), stride=(1,))
-      (1): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
+      (0): Conv1d(5, 256, kernel_size=(1,), stride=(1,))
+      (1): Conv1d(256, 32, kernel_size=(1,), stride=(1,))
     )
   )
   (projection): ChannelMLP(
@@ -297,13 +276,13 @@
 )
 
 ### SCHEDULER ###
- <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f746f238df0>
+ <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fea8ebc0ee0>
 
 ### LOSSES ###
 
- * Train: <neuralop.losses.data_losses.LpLoss object at 0x7f746c5a3d90>
+ * Train: <neuralop.losses.data_losses.LpLoss object at 0x7fea8ec2a2e0>
 
- * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f746c5a3d90>}
+ * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fea8ec2a2e0>}
 
    
 
    
 
    Create the trainer
    
@@ -328,22 +307,22 @@
 
    Training on 200 samples
 Testing on [50, 50] samples         on resolutions [(32, 64), (64, 128)].
 Raw outputs of shape torch.Size([4, 3, 32, 64])
-[0] time=0.91, avg_loss=2.6421, train_err=10.5682
-Eval: (32, 64)_l2=2.1051, (64, 128)_l2=2.4542
-[3] time=0.84, avg_loss=0.3812, train_err=1.5250
-Eval: (32, 64)_l2=0.5328, (64, 128)_l2=2.3434
-[6] time=0.73, avg_loss=0.2580, train_err=1.0322
-Eval: (32, 64)_l2=0.5245, (64, 128)_l2=2.2983
-[9] time=0.73, avg_loss=0.2163, train_err=0.8652
-Eval: (32, 64)_l2=0.4739, (64, 128)_l2=2.2781
-[12] time=0.73, avg_loss=0.1938, train_err=0.7753
-Eval: (32, 64)_l2=0.4735, (64, 128)_l2=2.2642
-[15] time=0.73, avg_loss=0.1628, train_err=0.6512
-Eval: (32, 64)_l2=0.4826, (64, 128)_l2=2.2505
-[18] time=0.73, avg_loss=0.1391, train_err=0.5565
-Eval: (32, 64)_l2=0.4369, (64, 128)_l2=2.2668
-
-{'train_err': 0.5427721077203751, 'avg_loss': 0.13569302693009377, 'avg_lasso_loss': None, 'epoch_train_time': 0.7358115278184414}
+[0] time=3.46, avg_loss=2.2655, train_err=9.0619
+Eval: (32, 64)_l2=1.6165, (64, 128)_l2=2.6751
+[3] time=3.47, avg_loss=0.4604, train_err=1.8414
+Eval: (32, 64)_l2=0.5567, (64, 128)_l2=2.3834
+[6] time=3.44, avg_loss=0.3189, train_err=1.2755
+Eval: (32, 64)_l2=0.4640, (64, 128)_l2=2.3548
+[9] time=3.41, avg_loss=0.2569, train_err=1.0278
+Eval: (32, 64)_l2=0.4651, (64, 128)_l2=2.3221
+[12] time=3.41, avg_loss=0.2121, train_err=0.8484
+Eval: (32, 64)_l2=0.3232, (64, 128)_l2=2.3157
+[15] time=3.40, avg_loss=0.1598, train_err=0.6390
+Eval: (32, 64)_l2=0.2673, (64, 128)_l2=2.3022
+[18] time=3.44, avg_loss=0.1432, train_err=0.5729
+Eval: (32, 64)_l2=0.2376, (64, 128)_l2=2.3091
+
+{'train_err': 0.550230712890625, 'avg_loss': 0.13755767822265624, 'avg_lasso_loss': None, 'epoch_train_time': 3.4114261440000746}
 
    
 
    
 
    Plot the prediction, and compare with the ground-truth
@@ -390,7 +369,7 @@
 fig.show()
 
    
 
    
-Inputs, ground-truth output and prediction., Input x (32, 64), Ground-truth y, Model prediction, Input x (64, 128), Ground-truth y, Model prediction
    Total running time of the script: (0 minutes 20.521 seconds)
    
+Inputs, ground-truth output and prediction., Input x (32, 64), Ground-truth y, Model prediction, Input x (64, 128), Ground-truth y, Model prediction
    Total running time of the script: (1 minutes 25.030 seconds)
    
 
    
 
    
-
    Loading test db for resolution 16 with 100 samples
+
    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  data = torch.load(
+Loading test db for resolution 16 with 100 samples
+/home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
 Loading test db for resolution 32 with 50 samples
-fno_skip='linear'
-channel_mlp_skip='linear'
-fno_skip='linear'
-channel_mlp_skip='linear'
-fno_skip='linear'
-channel_mlp_skip='linear'
-fno_skip='linear'
-channel_mlp_skip='linear'
-fno_skip='linear'
-channel_mlp_skip='linear'
-
-Our model has 2700097 parameters.
+
+Our model has 2665921 parameters.
 
    
 
    
 
    Create the optimizer
    
@@ -263,9 +250,9 @@
   )
   (fno_blocks): ModuleList(
     (0): FNOBlocks(
-      (convs): ModuleList(
-        (0): SpectralConv(
-          (weight): DenseTensor(shape=torch.Size([64, 32, 16, 9]), rank=None)
+      (convs): SpectralConv(
+        (weight): ModuleList(
+          (0): DenseTensor(shape=torch.Size([64, 32, 16, 9]), rank=None)
         )
       )
       (fno_skips): ModuleList(
@@ -273,24 +260,11 @@
           (conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,), bias=False)
         )
       )
-      (channel_mlp): ModuleList(
-        (0): ChannelMLP(
-          (fcs): ModuleList(
-            (0): Conv1d(32, 16, kernel_size=(1,), stride=(1,))
-            (1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))
-          )
-        )
-      )
-      (channel_mlp_skips): ModuleList(
-        (0): Flattened1dConv(
-          (conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,), bias=False)
-        )
-      )
     )
     (1): FNOBlocks(
-      (convs): ModuleList(
-        (0): SpectralConv(
-          (weight): DenseTensor(shape=torch.Size([32, 64, 8, 5]), rank=None)
+      (convs): SpectralConv(
+        (weight): ModuleList(
+          (0): DenseTensor(shape=torch.Size([32, 64, 8, 5]), rank=None)
         )
       )
       (fno_skips): ModuleList(
@@ -298,24 +272,11 @@
           (conv): Conv1d(32, 64, kernel_size=(1,), stride=(1,), bias=False)
         )
       )
-      (channel_mlp): ModuleList(
-        (0): ChannelMLP(
-          (fcs): ModuleList(
-            (0): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
-            (1): Conv1d(32, 64, kernel_size=(1,), stride=(1,))
-          )
-        )
-      )
-      (channel_mlp_skips): ModuleList(
-        (0): Flattened1dConv(
-          (conv): Conv1d(32, 64, kernel_size=(1,), stride=(1,), bias=False)
-        )
-      )
     )
     (2): FNOBlocks(
-      (convs): ModuleList(
-        (0): SpectralConv(
-          (weight): DenseTensor(shape=torch.Size([64, 64, 8, 5]), rank=None)
+      (convs): SpectralConv(
+        (weight): ModuleList(
+          (0): DenseTensor(shape=torch.Size([64, 64, 8, 5]), rank=None)
         )
       )
       (fno_skips): ModuleList(
@@ -323,24 +284,11 @@
           (conv): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
         )
       )
-      (channel_mlp): ModuleList(
-        (0): ChannelMLP(
-          (fcs): ModuleList(
-            (0): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
-            (1): Conv1d(32, 64, kernel_size=(1,), stride=(1,))
-          )
-        )
-      )
-      (channel_mlp_skips): ModuleList(
-        (0): Flattened1dConv(
-          (conv): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
-        )
-      )
     )
     (3): FNOBlocks(
-      (convs): ModuleList(
-        (0): SpectralConv(
-          (weight): DenseTensor(shape=torch.Size([128, 64, 8, 5]), rank=None)
+      (convs): SpectralConv(
+        (weight): ModuleList(
+          (0): DenseTensor(shape=torch.Size([128, 64, 8, 5]), rank=None)
         )
       )
       (fno_skips): ModuleList(
@@ -348,24 +296,11 @@
           (conv): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
         )
       )
-      (channel_mlp): ModuleList(
-        (0): ChannelMLP(
-          (fcs): ModuleList(
-            (0): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
-            (1): Conv1d(32, 64, kernel_size=(1,), stride=(1,))
-          )
-        )
-      )
-      (channel_mlp_skips): ModuleList(
-        (0): Flattened1dConv(
-          (conv): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
-        )
-      )
     )
     (4): FNOBlocks(
-      (convs): ModuleList(
-        (0): SpectralConv(
-          (weight): DenseTensor(shape=torch.Size([96, 32, 16, 9]), rank=None)
+      (convs): SpectralConv(
+        (weight): ModuleList(
+          (0): DenseTensor(shape=torch.Size([96, 32, 16, 9]), rank=None)
         )
       )
       (fno_skips): ModuleList(
@@ -373,19 +308,6 @@
           (conv): Conv1d(96, 32, kernel_size=(1,), stride=(1,), bias=False)
         )
       )
-      (channel_mlp): ModuleList(
-        (0): ChannelMLP(
-          (fcs): ModuleList(
-            (0): Conv1d(32, 16, kernel_size=(1,), stride=(1,))
-            (1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))
-          )
-        )
-      )
-      (channel_mlp_skips): ModuleList(
-        (0): Flattened1dConv(
-          (conv): Conv1d(96, 32, kernel_size=(1,), stride=(1,), bias=False)
-        )
-      )
     )
   )
   (horizontal_skips): ModuleDict(
@@ -416,13 +338,13 @@
 )
 
 ### SCHEDULER ###
- <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f2f6dd18a00>
+ <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7feab5259a00>
 
 ### LOSSES ###
 
- * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f2f6dee84f0>
+ * Train: <neuralop.losses.data_losses.H1Loss object at 0x7feab5259dc0>
 
- * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f2f6dee84f0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f2f6dd4d5a0>}
+ * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7feab5259dc0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7feab52594c0>}
 
    
 
    
 
    Create the trainer
    
@@ -449,22 +371,22 @@
 
    Training on 1000 samples
 Testing on [50, 50] samples         on resolutions [16, 32].
 Raw outputs of shape torch.Size([32, 1, 16, 16])
-[0] time=4.61, avg_loss=0.6586, train_err=20.5798
-Eval: 16_h1=0.4974, 16_l2=0.4004, 32_h1=0.8209, 32_l2=0.6873
-[3] time=4.63, avg_loss=0.2522, train_err=7.8821
-Eval: 16_h1=0.2281, 16_l2=0.1702, 32_h1=0.7338, 32_l2=0.5956
-[6] time=4.52, avg_loss=0.2534, train_err=7.9194
-Eval: 16_h1=0.2041, 16_l2=0.1527, 32_h1=0.6873, 32_l2=0.5407
-[9] time=4.44, avg_loss=0.2240, train_err=6.9998
-Eval: 16_h1=0.2075, 16_l2=0.1543, 32_h1=0.6566, 32_l2=0.4983
-[12] time=4.35, avg_loss=0.2071, train_err=6.4715
-Eval: 16_h1=0.1967, 16_l2=0.1384, 32_h1=0.6875, 32_l2=0.5304
-[15] time=4.36, avg_loss=0.1711, train_err=5.3483
-Eval: 16_h1=0.2300, 16_l2=0.1629, 32_h1=0.6453, 32_l2=0.4576
-[18] time=4.43, avg_loss=0.1268, train_err=3.9616
-Eval: 16_h1=0.1860, 16_l2=0.1351, 32_h1=0.6503, 32_l2=0.4821
-
-{'train_err': 3.9999673664569855, 'avg_loss': 0.12799895572662354, 'avg_lasso_loss': None, 'epoch_train_time': 4.439005568623543}
+[0] time=6.61, avg_loss=0.5430, train_err=16.9693
+Eval: 16_h1=0.3210, 16_l2=0.2597, 32_h1=0.7922, 32_l2=0.5999
+[3] time=6.48, avg_loss=0.2685, train_err=8.3897
+Eval: 16_h1=0.2068, 16_l2=0.1545, 32_h1=0.7909, 32_l2=0.6063
+[6] time=6.47, avg_loss=0.2368, train_err=7.4001
+Eval: 16_h1=0.2155, 16_l2=0.1649, 32_h1=0.7626, 32_l2=0.5989
+[9] time=6.47, avg_loss=0.2172, train_err=6.7885
+Eval: 16_h1=0.2323, 16_l2=0.1762, 32_h1=0.7456, 32_l2=0.5858
+[12] time=6.48, avg_loss=0.2048, train_err=6.3987
+Eval: 16_h1=0.1903, 16_l2=0.1337, 32_h1=0.7315, 32_l2=0.5729
+[15] time=6.46, avg_loss=0.1886, train_err=5.8936
+Eval: 16_h1=0.1910, 16_l2=0.1350, 32_h1=0.7238, 32_l2=0.5652
+[18] time=6.48, avg_loss=0.1804, train_err=5.6383
+Eval: 16_h1=0.1816, 16_l2=0.1254, 32_h1=0.7171, 32_l2=0.5568
+
+{'train_err': 5.1316939406096935, 'avg_loss': 0.16421420609951018, 'avg_lasso_loss': None, 'epoch_train_time': 6.497358972000029}
 
    
 
    
 
    Plot the prediction, and compare with the ground-truth
@@ -514,7 +436,7 @@
 fig.show()
 
    
 
    
-Inputs, ground-truth output and prediction., Input x, Ground-truth y, Model prediction
    Total running time of the script: (1 minutes 33.254 seconds)
    
+Inputs, ground-truth output and prediction., Input x, Ground-truth y, Model prediction
    Total running time of the script: (2 minutes 12.491 seconds)
    
 
    
 
    
-
    Loading test db for resolution 16 with 50 samples
+
    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  data = torch.load(
+Loading test db for resolution 16 with 50 samples
+/home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
 Original dataset shape torch.Size([50, 1, 16, 16])
 
    
 
    
@@ -280,11 +284,11 @@ 
    Import the libraryplt.show()
 
    
 
    
-Spectrum of Darcy Flow Datset
    /home/dave/myneurop/examples/plot_darcy_flow_spectrum.py:104: UserWarning: Attempt to set non-positive ylim on a log-scaled axis will be ignored.
+Spectrum of Darcy Flow Datset
    /home/runner/work/neuraloperator/neuraloperator/examples/plot_darcy_flow_spectrum.py:104: UserWarning: Attempt to set non-positive ylim on a log-scaled axis will be ignored.
   ax.set_ylim(10, 10^10)
 
    
 
    
-
    Total running time of the script: (0 minutes 0.139 seconds)
    
+
    Total running time of the script: (0 minutes 0.279 seconds)
    
     
 
    Gallery generated by Sphinx-Gallery
    
 
diff --git a/dev/auto_examples/plot_incremental_FNO_darcy.html b/dev/auto_examples/plot_incremental_FNO_darcy.html
index cad7e1b..d92910e 100644
--- a/dev/auto_examples/plot_incremental_FNO_darcy.html
+++ b/dev/auto_examples/plot_incremental_FNO_darcy.html
@@ -17,7 +17,7 @@
 
     
     
-    
+    
     
     
     
@@ -131,7 +131,7 @@
 
  • Using torchtnt to count FLOPS
    • 
 
  • A simple Darcy-Flow dataset
    • 
 
  • Training a TFNO on Darcy-Flow
    • 
-
  • Training an FNO on Darcy-Flow
    • 
+
  • Training a TFNO on Darcy-Flow
    • 
 
  • Training a SFNO on the spherical Shallow Water equations
    • 
 
  • U-NO on Darcy-Flow
    • 
 
  • A simple Darcy-Flow spectrum analysis
    • 
@@ -192,7 +192,11 @@
 )
 
    
 
    
-
    Loading test db for resolution 16 with 100 samples
+
    /home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:93: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  data = torch.load(
+Loading test db for resolution 16 with 100 samples
+/home/runner/work/neuraloperator/neuraloperator/neuralop/data/datasets/pt_dataset.py:172: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  data = torch.load(Path(root_dir).joinpath(f"{dataset_name}_test_{res}.pt").as_posix())
 Loading test db for resolution 32 with 50 samples
 
    
 
    
@@ -271,7 +275,7 @@
 
    
 
    
 
    ### N PARAMS ###
- 2110305
+ 2119329
 
 ### OPTIMIZER ###
  AdamW (
@@ -285,15 +289,15 @@
 )
 
 ### SCHEDULER ###
- <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f746c2a2b90>
+ <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fea8ec5f790>
 
 ### LOSSES ###
 
 ### INCREMENTAL RESOLUTION + GRADIENT EXPLAINED ###
 
- * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f746c3ab1f0>
+ * Train: <neuralop.losses.data_losses.H1Loss object at 0x7feab84cca90>
 
- * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f746c3ab1f0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f746c2a2fe0>}
+ * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7feab84cca90>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7feaa0415d00>}
 
    
 
    
 
    Set up the IncrementalTrainer
@@ -347,51 +351,51 @@
 
    Training on 100 samples
 Testing on [50, 50] samples         on resolutions [16, 32].
 Raw outputs of shape torch.Size([16, 1, 8, 8])
-[0] time=0.07, avg_loss=0.8236, train_err=11.7655
-Eval: 16_h1=0.7665, 16_l2=0.6166, 32_h1=0.7932, 32_l2=0.6111
-[1] time=0.03, avg_loss=0.6737, train_err=9.6245
-Eval: 16_h1=0.6417, 16_l2=0.4694, 32_h1=0.7263, 32_l2=0.4793
-[2] time=0.03, avg_loss=0.5479, train_err=7.8269
-Eval: 16_h1=0.6615, 16_l2=0.4694, 32_h1=0.8183, 32_l2=0.4896
-[3] time=0.03, avg_loss=0.5040, train_err=7.1998
-Eval: 16_h1=0.5634, 16_l2=0.4002, 32_h1=0.6886, 32_l2=0.4146
-[4] time=0.04, avg_loss=0.4482, train_err=6.4026
-Eval: 16_h1=0.5917, 16_l2=0.4267, 32_h1=0.7822, 32_l2=0.4703
-[5] time=0.04, avg_loss=0.4120, train_err=5.8851
-Eval: 16_h1=0.5466, 16_l2=0.3766, 32_h1=0.7625, 32_l2=0.4164
-[6] time=0.04, avg_loss=0.3683, train_err=5.2609
-Eval: 16_h1=0.4672, 16_l2=0.3235, 32_h1=0.6485, 32_l2=0.3597
-[7] time=0.04, avg_loss=0.3326, train_err=4.7513
-Eval: 16_h1=0.4938, 16_l2=0.3391, 32_h1=0.6948, 32_l2=0.3841
-[8] time=0.04, avg_loss=0.3064, train_err=4.3769
-Eval: 16_h1=0.4673, 16_l2=0.3198, 32_h1=0.6777, 32_l2=0.3651
-[9] time=0.04, avg_loss=0.2868, train_err=4.0965
-Eval: 16_h1=0.4539, 16_l2=0.3072, 32_h1=0.6651, 32_l2=0.3536
+[0] time=0.21, avg_loss=0.7750, train_err=11.0714
+Eval: 16_h1=0.7031, 16_l2=0.5348, 32_h1=0.7319, 32_l2=0.5357
+[1] time=0.21, avg_loss=0.5908, train_err=8.4395
+Eval: 16_h1=0.6114, 16_l2=0.4391, 32_h1=0.6716, 32_l2=0.4473
+[2] time=0.20, avg_loss=0.5093, train_err=7.2754
+Eval: 16_h1=0.5647, 16_l2=0.3843, 32_h1=0.6667, 32_l2=0.3946
+[3] time=0.21, avg_loss=0.4408, train_err=6.2975
+Eval: 16_h1=0.5216, 16_l2=0.3600, 32_h1=0.6661, 32_l2=0.3915
+[4] time=0.21, avg_loss=0.4055, train_err=5.7927
+Eval: 16_h1=0.5165, 16_l2=0.3631, 32_h1=0.6852, 32_l2=0.4008
+[5] time=0.21, avg_loss=0.3794, train_err=5.4201
+Eval: 16_h1=0.5407, 16_l2=0.4053, 32_h1=0.6456, 32_l2=0.4213
+[6] time=0.22, avg_loss=0.3662, train_err=5.2311
+Eval: 16_h1=0.4848, 16_l2=0.3434, 32_h1=0.6641, 32_l2=0.3786
+[7] time=0.22, avg_loss=0.3320, train_err=4.7433
+Eval: 16_h1=0.4515, 16_l2=0.3280, 32_h1=0.5890, 32_l2=0.3661
+[8] time=0.22, avg_loss=0.3013, train_err=4.3041
+Eval: 16_h1=0.4443, 16_l2=0.3024, 32_h1=0.6300, 32_l2=0.3467
+[9] time=0.23, avg_loss=0.2621, train_err=3.7436
+Eval: 16_h1=0.4252, 16_l2=0.2978, 32_h1=0.6085, 32_l2=0.3395
 Incre Res Update: change index to 1
 Incre Res Update: change sub to 1
 Incre Res Update: change res to 16
-[10] time=0.06, avg_loss=0.3922, train_err=5.6035
-Eval: 16_h1=0.3690, 16_l2=0.2683, 32_h1=0.4566, 32_l2=0.2656
-[11] time=0.04, avg_loss=0.3392, train_err=4.8454
-Eval: 16_h1=0.3419, 16_l2=0.2484, 32_h1=0.4551, 32_l2=0.2650
-[12] time=0.04, avg_loss=0.3098, train_err=4.4253
-Eval: 16_h1=0.3172, 16_l2=0.2389, 32_h1=0.4273, 32_l2=0.2547
-[13] time=0.04, avg_loss=0.2827, train_err=4.0392
-Eval: 16_h1=0.3155, 16_l2=0.2391, 32_h1=0.4182, 32_l2=0.2516
-[14] time=0.04, avg_loss=0.2774, train_err=3.9626
-Eval: 16_h1=0.2872, 16_l2=0.2186, 32_h1=0.3938, 32_l2=0.2344
-[15] time=0.04, avg_loss=0.2490, train_err=3.5571
-Eval: 16_h1=0.2744, 16_l2=0.2075, 32_h1=0.3745, 32_l2=0.2220
-[16] time=0.04, avg_loss=0.2346, train_err=3.3511
-Eval: 16_h1=0.2758, 16_l2=0.2056, 32_h1=0.3889, 32_l2=0.2249
-[17] time=0.04, avg_loss=0.2293, train_err=3.2760
-Eval: 16_h1=0.2793, 16_l2=0.2077, 32_h1=0.3904, 32_l2=0.2213
-[18] time=0.04, avg_loss=0.2262, train_err=3.2321
-Eval: 16_h1=0.3024, 16_l2=0.2263, 32_h1=0.4136, 32_l2=0.2417
-[19] time=0.04, avg_loss=0.2388, train_err=3.4117
-Eval: 16_h1=0.3040, 16_l2=0.2327, 32_h1=0.4129, 32_l2=0.2462
-
-{'train_err': 3.411697438785008, 'avg_loss': 0.23881882071495056, 'avg_lasso_loss': None, 'epoch_train_time': 0.04143996722996235, '16_h1': tensor(0.3040, device='cuda:0'), '16_l2': tensor(0.2327, device='cuda:0'), '32_h1': tensor(0.4129, device='cuda:0'), '32_l2': tensor(0.2462, device='cuda:0')}
+[10] time=0.30, avg_loss=0.3530, train_err=5.0422
+Eval: 16_h1=0.3418, 16_l2=0.2496, 32_h1=0.4258, 32_l2=0.2477
+[11] time=0.29, avg_loss=0.2891, train_err=4.1300
+Eval: 16_h1=0.3833, 16_l2=0.2783, 32_h1=0.4696, 32_l2=0.2820
+[12] time=0.31, avg_loss=0.2975, train_err=4.2504
+Eval: 16_h1=0.3179, 16_l2=0.2267, 32_h1=0.4156, 32_l2=0.2404
+[13] time=0.31, avg_loss=0.2420, train_err=3.4567
+Eval: 16_h1=0.2829, 16_l2=0.2034, 32_h1=0.3807, 32_l2=0.2174
+[14] time=0.31, avg_loss=0.2147, train_err=3.0676
+Eval: 16_h1=0.3394, 16_l2=0.2630, 32_h1=0.4255, 32_l2=0.2714
+[15] time=0.32, avg_loss=0.2232, train_err=3.1885
+Eval: 16_h1=0.3785, 16_l2=0.2985, 32_h1=0.4668, 32_l2=0.3102
+[16] time=0.32, avg_loss=0.2555, train_err=3.6494
+Eval: 16_h1=0.3279, 16_l2=0.2593, 32_h1=0.4078, 32_l2=0.2623
+[17] time=0.32, avg_loss=0.2769, train_err=3.9559
+Eval: 16_h1=0.4073, 16_l2=0.3371, 32_h1=0.4499, 32_l2=0.3422
+[18] time=0.32, avg_loss=0.2840, train_err=4.0576
+Eval: 16_h1=0.2826, 16_l2=0.2202, 32_h1=0.3649, 32_l2=0.2272
+[19] time=0.32, avg_loss=0.1984, train_err=2.8340
+Eval: 16_h1=0.2795, 16_l2=0.2186, 32_h1=0.3582, 32_l2=0.2296
+
+{'train_err': 2.8339713641575406, 'avg_loss': 0.19837799549102783, 'avg_lasso_loss': None, 'epoch_train_time': 0.3178346639999745, '16_h1': tensor(0.2795), '16_l2': tensor(0.2186), '32_h1': tensor(0.3582), '32_l2': tensor(0.2296)}
 
    
 
    
 
    Plot the prediction, and compare with the ground-truth
@@ -441,7 +445,7 @@
 fig.show()
 
    
 
    
-Inputs, ground-truth output and prediction., Input x, Ground-truth y, Model prediction
    Total running time of the script: (0 minutes 1.140 seconds)
    
+Inputs, ground-truth output and prediction., Input x, Ground-truth y, Model prediction
    Total running time of the script: (0 minutes 7.776 seconds)