Provided below are a line of tutorials and guides that quickly highlight key modules, features, and resources. Great for new users of PyDMD.

Name	Description	PyDMD used classes
Manual1 [.ipynb, .py]	The Basics of BOPDMD	pydmd.BOPDMD

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
Tutorial19 [.ipynb, .py, .html]	HAVOK for modeling chaos with partial measurements	pydmd.HAVOK
Tutorial20a [.ipynb]	COSTS for decomposing toy data	pydmd.COSTS
Tutorial20b [.ipynb]	mrCOSTS for decomposing multi-scale physics of real, noisy data	pydmd.mrCOSTS

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!