用scikit-learn风格实现动力系统算子学习，支持时序预测与降维建模
kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
- 基于线性、核方法和深度学习，学习动力系统的演化算子与谱分解
- 可建模离散与连续时间系统，支持状态与可观测量的未来预测
- 接口兼容scikit-learn，附带基准数据集，便于算法对比与复现
kooplearn 是一个机器学习库，实现了动力系统演化算子及其谱分解的线性、核方法和深度学习估计器。该库可建模离散时间演化算子（Koopman/转移算子）与连续时间无穷小生成算子。通过学习这些算子，用户可利用谱方法分析动力系统，构建数据驱动的降阶模型，并预测未来状态与可观测变量。kooplearn 接口兼容 scikit-learn，便于集成至现有机器学习与数据科学流程。此外，库中包含经过整理的基准数据集，以支持实验、可复现性及算法公平比较。软件开源地址：https://github.com/Machine-Learning-Dynamical-Systems/kooplearn。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。