Fix Tests and Cleanup

.JuliaFormatter.toml

@@ -1,2 +1,4 @@
 style = "sciml"
-format_markdown = true
+annotate_untyped_fields_with_any = false
+format_markdown = true
+separate_kwargs_with_semicolon = true

.buildkite/pipeline.yml

@@ -27,6 +27,7 @@ steps:
         using Pkg
         Pkg.instantiate()
         Pkg.activate("docs")
+        Pkg.develop(PackageSpec(path=pwd()))
         Pkg.instantiate()
         push!(LOAD_PATH, @__DIR__)
         println("+++ :julia: Building documentation")

.github/workflows/CI.yml

@@ -21,8 +21,10 @@ jobs:
           - BasicNeuralDE
           - AdvancedNeuralDE
           - Newton
+          - Aqua
         version:
-          - 1
+          - '1'
+          - '~1.10.0-0'
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v1

Project.toml

@@ -1,53 +1,53 @@
 name = "DiffEqFlux"
 uuid = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 authors = ["Chris Rackauckas <[email protected]>"]
-version = "2.5.1"
+version = "3.0.0"
 
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-ConsoleProgressMonitor = "88cd18e8-d9cc-4ea6-8889-5259c0d15c8b"
-DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
+ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
+ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
-LoggingExtras = "e6f89c97-d47a-5376-807f-9c37f3926c36"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
+PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
-TerminalLoggers = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
+Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [compat]
+ADTypes = "0.2"
 Adapt = "3"
 ChainRulesCore = "1"
-ConsoleProgressMonitor = "0.1"
-DataInterpolations = "3.3, 4"
+ComponentArrays = "0.15.5"
+ConcreteStructs = "0.2"
 DiffEqBase = "6.41"
 Distributions = "0.23, 0.24, 0.25"
 DistributionsAD = "0.6"
-Flux = "0.14"
 ForwardDiff = "0.10"
 Functors = "0.4"
-LoggingExtras = "0.4, 1"
+LinearAlgebra = "<0.0.1, 1"
+Lux = "0.5.5"
 LuxCore = "0.1"
-ProgressLogging = "0.1"
+PrecompileTools = "1"
+Random = "<0.0.1, 1"
 RecursiveArrayTools = "2"
 Reexport = "0.2, 1"
 SciMLBase = "1, 2"
 SciMLSensitivity = "7"
-TerminalLoggers = "0.1"
+Tracker = "0.2.29"
 Zygote = "0.5, 0.6"
 ZygoteRules = "0.2"
 julia = "1.9"

README.md

@@ -7,16 +7,16 @@
 [![Build Status](https://github.com/SciML/DiffEqFlux.jl/workflows/CI/badge.svg)](https://github.com/SciML/DiffEqFlux.jl/actions?query=workflow%3ACI)
 [![Build status](https://badge.buildkite.com/a1fecf87b085b452fe0f3d3968ddacb5c1d5570806834e1d52.svg)](https://buildkite.com/julialang/diffeqflux-dot-jl)
 
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
-DiffEqFlux.jl fuses the world of differential equations with machine learning
+DiffEq(For)Lux.jl (aka DiffEqFlux.jl) fuses the world of differential equations with machine learning
 by helping users put diffeq solvers into neural networks. This package utilizes
-[DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/),
-[Flux.jl](https://docs.sciml.ai/Flux/stable/) and [Lux.jl](https://lux.csail.mit.edu/v0.5.5/api/)  as its building blocks to support research in
-[Scientific Machine Learning](https://www.stochasticlifestyle.com/the-essential-tools-of-scientific-machine-learning-scientific-ml/),
-specifically neural differential equations and universal differential equations,
-to add physical information into traditional machine learning.
+[DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/), and [Lux.jl](https://lux.csail.mit.edu/)  as its building blocks to support research in
+[Scientific Machine Learning](https://www.stochasticlifestyle.com/the-essential-tools-of-scientific-machine-learning-scientific-ml/), specifically neural differential equations and universal differential equations, to add physical information into traditional machine learning.
+
+> [!NOTE]
+> We maintain backwards compatibility with [Flux.jl](https://docs.sciml.ai/Flux/stable/) via [Lux.transform](https://lux.csail.mit.edu/stable/api/Lux/flux_to_lux#Lux.transform)
 
 ## Tutorials and Documentation
 
@@ -40,12 +40,12 @@ Scientific Machine Learning](https://arxiv.org/abs/2001.04385).
 
 As such, it is the first package to support and demonstrate:
 
-- Stiff and non-stiff universal ordinary differential equations (universal ODEs)
-- Universal stochastic differential equations (universal SDEs)
-- Universal delay differential equations (universal DDEs)
-- Universal partial differential equations (universal PDEs)
-- Universal jump stochastic differential equations (universal jump diffusions)
-- Hybrid universal differential equations (universal DEs with event handling)
+  - Stiff and non-stiff universal ordinary differential equations (universal ODEs)
+  - Universal stochastic differential equations (universal SDEs)
+  - Universal delay differential equations (universal DDEs)
+  - Universal partial differential equations (universal PDEs)
+  - Universal jump stochastic differential equations (universal jump diffusions)
+  - Hybrid universal differential equations (universal DEs with event handling)
 
 with high order, adaptive, implicit, GPU-accelerated, Newton-Krylov, etc.
 methods. For examples, please refer to
@@ -58,10 +58,18 @@ PDEs and neural jump SDEs, can be found
 Do not limit yourself to the current neuralization. With this package, you can
 explore various ways to integrate the two methodologies:
 
-- Neural networks can be defined where the “activations” are nonlinear functions
-  described by differential equations
-- Neural networks can be defined where some layers are ODE solves
-- ODEs can be defined where some terms are neural networks
-- Cost functions on ODEs can define neural networks
+  - Neural networks can be defined where the “activations” are nonlinear functions
+    described by differential equations
+  - Neural networks can be defined where some layers are ODE solves
+  - ODEs can be defined where some terms are neural networks
+  - Cost functions on ODEs can define neural networks
 
 ![Flux ODE Training Animation](https://user-images.githubusercontent.com/1814174/88589293-e8207f80-d026-11ea-86e2-8a3feb8252ca.gif)
+
+## Breaking Changes in v3
+
+  - Flux dependency is dropped. If a non Lux `AbstractExplicitLayer` is passed we try to automatically convert it to a Lux model with `Lux.transform(model)`.
+  - `Flux` is no longer re-exported from `DiffEqFlux`. Instead we reexport `Lux`.
+  - `NeuralDAE` now allows an optional `du0` as input.
+  - `TensorLayer` is now a Lux Neural Network.
+  - APIs for quite a few layer constructions have changed. Please refer to the updated documentation for more details.

docs/Project.toml

@@ -1,10 +1,10 @@
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 DataDeps = "124859b0-ceae-595e-8997-d05f6a7a8dfe"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -13,49 +13,61 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
 MLDataUtils = "cc2ba9b6-d476-5e6d-8eaf-a92d5412d41d"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 OptimizationPolyalgorithms = "500b13db-7e66-49ce-bda4-eed966be6282"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 CSV = "0.10"
 ComponentArrays = "0.13, 0.14, 0.15"
 DataDeps = "0.7"
 DataFrames = "1"
-DiffEqFlux = "2"
-DifferentialEquations = "7.6.0"
+DiffEqFlux = "3"
 Distances = "0.10.7"
 Distributions = "0.25.78"
 Documenter = "1"
 Flux = "0.14"
 ForwardDiff = "0.10"
 IterTools = "1"
-Lux = "0.4.34, 0.5"
+LinearAlgebra = "<0.0.1, 1"
+Lux = "0.5.5"
+LuxCUDA = "0.3"
 MLDataUtils = "0.5"
 MLDatasets = "0.7"
+MLUtils = "0.4"
+NNlib = "0.9"
 Optimisers = "0.2, 0.3"
 Optimization = "3.9"
 OptimizationOptimJL = "0.1"
 OptimizationOptimisers = "0.1"
 OptimizationPolyalgorithms = "0.1"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
+Printf = "<0.0.1, 1"
+Random = "<0.0.1, 1"
 ReverseDiff = "1.14"
 SciMLBase = "1.72, 2"
 SciMLSensitivity = "7.11"
 Statistics = "1"
 StochasticDiffEq = "6.56"
+Test = "<0.0.1, 1"
 Zygote = "0.6.62"

docs/make.jl

@@ -1,26 +1,22 @@
 using Documenter, DiffEqFlux
 
-cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
-cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
+cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml"; force = true)
+cp("./docs/Project.toml", "./docs/src/assets/Project.toml"; force = true)
 
 ENV["GKSwstype"] = "100"
 using Plots
 ENV["DATADEPS_ALWAYS_ACCEPT"] = true
 
 include("pages.jl")
 
-makedocs(
-    sitename = "DiffEqFlux.jl",
-    authors="Chris Rackauckas et al.",
+makedocs(; sitename = "DiffEqFlux.jl",
+    authors = "Chris Rackauckas et al.",
     clean = true, doctest = false, linkcheck = true,
     warnonly = [:docs_block, :missing_docs],
     modules = [DiffEqFlux],
-    format = Documenter.HTML(assets = ["assets/favicon.ico"],
-                             canonical="https://docs.sciml.ai/DiffEqFlux/stable/"),
-    pages=pages
-)
+    format = Documenter.HTML(; assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/DiffEqFlux/stable/"),
+    pages = pages)
 
-deploydocs(
-   repo = "github.com/SciML/DiffEqFlux.jl.git";
-   push_preview = true
-)
+deploydocs(; repo = "github.com/SciML/DiffEqFlux.jl.git",
+    push_preview = true)

docs/pages.jl

@@ -1,29 +1,23 @@
 pages = [
-        "DiffEqFlux.jl: High Level Scientific Machine Learning (SciML) Pre-Built Architectures" => "index.md",
-        "Differential Equation Machine Learning Tutorials" => Any[
-            "examples/neural_ode.md",
-            "examples/GPUs.md",
-            "examples/mnist_neural_ode.md",
-            "examples/mnist_conv_neural_ode.md",
-            "examples/augmented_neural_ode.md",
-            "examples/neural_sde.md",
-            "examples/collocation.md",
-            "examples/normalizing_flows.md",
-            "examples/hamiltonian_nn.md",
-            "examples/tensor_layer.md",
-            "examples/multiple_shooting.md",
-            "examples//neural_ode_weather_forecast.md"
-        ],
-        "Layer APIs" => Any[
-            "Classical Basis Layers" => "layers/BasisLayers.md",
-            "Tensor Product Layer" => "layers/TensorLayer.md",
-            "Continuous Normalizing Flows Layer" => "layers/CNFLayer.md",
-            "Spline Layer" => "layers/SplineLayer.md",
-            "Neural Differential Equation Layers" => "layers/NeuralDELayers.md",
-            "Hamiltonian Neural Network Layer" => "layers/HamiltonianNN.md"
-        ],
-        "Utility Function APIs" => Any[
-            "Smoothed Collocation" => "utilities/Collocation.md",
-            "Multiple Shooting Functionality" => "utilities/MultipleShooting.md",
-        ],
-    ]
+    "DiffEqFlux.jl: High Level Scientific Machine Learning (SciML) Pre-Built Architectures" => "index.md",
+    "Differential Equation Machine Learning Tutorials" => Any["examples/neural_ode.md",
+        "examples/GPUs.md",
+        "examples/mnist_neural_ode.md",
+        "examples/mnist_conv_neural_ode.md",
+        "examples/augmented_neural_ode.md",
+        "examples/neural_sde.md",
+        "examples/collocation.md",
+        "examples/normalizing_flows.md",
+        "examples/hamiltonian_nn.md",
+        "examples/tensor_layer.md",
+        "examples/multiple_shooting.md",
+        "examples//neural_ode_weather_forecast.md"],
+    "Layer APIs" => Any["Classical Basis Layers" => "layers/BasisLayers.md",
+        "Tensor Product Layer" => "layers/TensorLayer.md",
+        "Continuous Normalizing Flows Layer" => "layers/CNFLayer.md",
+        "Spline Layer" => "layers/SplineLayer.md",
+        "Neural Differential Equation Layers" => "layers/NeuralDELayers.md",
+        "Hamiltonian Neural Network Layer" => "layers/HamiltonianNN.md"],
+    "Utility Function APIs" => Any["Smoothed Collocation" => "utilities/Collocation.md",
+        "Multiple Shooting Functionality" => "utilities/MultipleShooting.md"],
+]