kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning

TL;DR

kooplearn is a scikit-learn compatible library that enables learning and spectral analysis of dynamical operators, facilitating data-driven modeling, forecasting, and reduced-order modeling of dynamical systems.

Contribution

It introduces a library that implements various estimators for dynamical operators, integrating spectral methods with scikit-learn compatibility for easier adoption.

Findings

Supports modeling both discrete and continuous-time operators

Includes curated benchmark datasets for evaluation

Facilitates spectral analysis and forecasting of dynamical systems

Abstract

kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model both discrete-time evolution operators (Koopman/Transfer) and continuous-time infinitesimal generators. By learning these operators, users can analyze dynamical systems via spectral methods, derive data-driven reduced-order models, and forecast future states and observables. kooplearn's interface is compliant with the scikit-learn API, facilitating its integration into existing machine learning and data science workflows. Additionally, kooplearn includes curated benchmark datasets to support experimentation, reproducibility, and the fair comparison of learning algorithms. The software is available at https://github.com/Machine-Learning-Dynamical-Systems/kooplearn.