kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
This provides a tool for researchers and practitioners in machine learning and data science to analyze and predict dynamical systems, but it is incremental as it packages existing algorithms into a user-friendly library.
The authors introduced kooplearn, a library for learning evolution operators in dynamical systems, enabling analysis, reduced-order modeling, and forecasting through spectral methods, with compatibility to scikit-learn for integration into existing workflows.
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.