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Tutorials

In this folder we collect several useful tutorials in order to understand the principles and the potential of PyDMD. Please read the following table for details about the tutorials. An additional PDF tutorial (DSWeb contest winner) is available here.

Name Description PyDMD used classes
Tutorial1 [.ipynb.py.html] Analyzing real, simple data sets with PyDMD pydmd.DMD, pydmd.BOPDMD
Tutorial2 [.ipynb.py.html] advanced features of standard DMD pydmd.DMD
Tutorial3 [.ipynb.py.html] multi-resolution DMD for transient phenomena pydmd.MrDMD
Tutorial4 [.ipynb.py.html] compress DMD for computation speedup pydmd.CDMD
Tutorial5 [.ipynb.py.html] forward-backward DMD for CFD model analysis pydmd.FbDMD
Tutorial6 [.ipynb.py.html] higher-order DMD applied to scalar time-series pydmd.HODMD
Tutorial7 [.ipynb.py.html] DMD with control pydmd.DMDC
Tutorial8 [.ipynb.py.html] comparison between DMD and optimal closed-form DMD pydmd.OptDMD
Tutorial9 [.ipynb.py.html] sparsity-promoting DMD pydmd.SpDMD
Tutorial10 [.ipynb.py.html] parametric DMD pydmd.ParametricDMD
Tutorial11 [.ipynb.py.html] Tikhonov regularization) pydmd.DMDBase
Tutorial12 [.ipynb.py] cDMD for background modeling pydmd.CDMD
Tutorial13 [.ipynb.py] SubspaceDMD for locating eigenvalues of stochastic systems pydmd.SubspaceDMD
Tutorial14 [.ipynb.py.html] Comparison between Bagging-/ Optimized DMD and exact DMD pydmd.BOPDMD
Tutorial15 [.ipynb.py.html] Physics-informed DMD for manifold enforcement pydmd.PiDMD
Tutorial16 [.ipynb.py.html] Randomized DMD for greater computation speedup pydmd.RDMD
Tutorial17 [.ipynb.py.html] Extended DMD for nonlinear eigenfunction discovery pydmd.EDMD
Tutorial18 [.ipynb.py.html] LANDO for nonlinear system modeling pydmd.LANDO

Tutorials for Developers

We collect here also the resources for helping developers to contribute to PyDMD.

Name Description PyDMD used classes
Tutorial1 [.ipynb.py.html] implementing a new version of DMD pydmd.DMDBase

More to come...

We plan to add more tutorials but the time is often against us. If you want to contribute with a notebook on a feature not covered yet we will be very happy and give you support on editing!