PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator

• URL: http://arxiv.org/abs/2306.12962v1
• Date: Thu, 22 Jun 2023 16:55:01 GMT
• Title: PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
• Authors: Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton
• Abstract summary: PyKoopman is a Python package for the data-driven approximation of the Koopman operator associated with a dynamical system. In particular, PyKoopman provides tools for data-driven system identification for unforced and actuated systems.
• Score: 4.069849286089743
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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