PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
Abstract
PyKoopman is a Python package for the data-driven approximation of the Koopman operator associated with a dynamical system. The Koopman operator is a principled linear embedding of nonlinear dynamics and facilitates the prediction, estimation, and control of strongly nonlinear dynamics using linear systems theory. In particular, PyKoopman provides tools for data-driven system identification for unforced and actuated systems that build on the equation-free dynamic mode decomposition (DMD) and its variants. In this work, we provide a brief description of the mathematical underpinnings of the Koopman operator, an overview and demonstration of the features implemented in PyKoopman (with code examples), practical advice for users, and a list of potential extensions to PyKoopman. Software is available at http://github.com/dynamicslab/pykoopman
Cite
@article{arxiv.2306.12962,
title = {PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator},
author = {Shaowu Pan and Eurika Kaiser and Brian M. de Silva and J. Nathan Kutz and Steven L. Brunton},
journal= {arXiv preprint arXiv:2306.12962},
year = {2023}
}
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16 pages