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.

翻译：kooplearn 是一个机器学习库，它实现了动力学算子及其谱分解的线性、核和深度学习估计器。kooplearn 能够对离散时间演化算子（Koopman/Transfer）和连续时间无穷小生成元进行建模。通过学习这些算子，用户可以通过谱方法分析动力系统，推导数据驱动的降阶模型，并预测未来状态和可观测量。kooplearn 的接口遵循 scikit-learn API，便于其集成到现有的机器学习和数据科学工作流程中。此外，kooplearn 还包含精选的基准数据集，以支持实验、可重复性以及学习算法的公平比较。该软件可在 https://github.com/Machine-Learning-Dynamical-Systems/kooplearn 获取。