PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
This is an incremental software package for researchers and practitioners in dynamical systems and control, facilitating easier implementation of existing Koopman operator methods.
PyKoopman is a Python package for approximating the Koopman operator to enable linear analysis of nonlinear dynamical systems, providing tools for data-driven system identification based on dynamic mode decomposition variants.
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