PyDMD

PyDMD - Python library for Dynamic Mode Decomposition

PyDMD is a python library that uses Dynamic Mode Decomposition for a data-driven model simplification based on spatiotemporal coherent structures.

Dynamic Mode Decomposition (DMD) is a model reduction algorithm developed by Schmid (see "Dynamic mode decomposition of numerical and experimental data"). Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See Kutz ("Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems") for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in Koopman ("Hamiltonian systems and transformation in Hilbert space"), along with examples in computational fluid dynamics.

In the last years many variants arose, such as multiresolution DMD, compressed DMD, forward backward DMD, and higher order DMD among others, in order to deal with noisy data, big dataset, or spurius data for example.

The research in the field is growing both in computational fluid dynamic and in structural mechanics, due to the equation-free nature of the model.

PyDMD is freely available under the MIT License.

If you use the package please cite it as follows:

@article{demo18pydmd,
  Author = {Demo, Nicola and Tezzele, Marco and Rozza, Gianluigi},
  Title = {{PyDMD: Python Dynamic Mode Decomposition}},
  Journal = {The Journal of Open Source Software},
  Volume = {3},
  Number = {22},
  Pages = {530},
  Year = {2018},
  Doi = {https://doi.org/10.21105/joss.00530}
}

PyDMD is currently developed and mantained at SISSA mathLab by

under the supervision of Prof. Gianluigi Rozza.