DLKoopman：用于Koopman理论的深度学习软件包
DLKoopman: A deep learning software package for Koopman theory
AI总结：
DLKoopman是一个基于深度学习的通用软件包，用于学习非线性动力系统的线性编码和动力学，支持状态与轨迹预测，并提供新评估指标和超参数搜索模块。
摘要：
我们介绍了DLKoopman——一个用于Koopman理论的软件包，它利用深度学习来学习非线性动力系统到线性空间的编码，同时学习线性动力学。虽然之前的一些工作要么限制了学习编码的能力，要么是针对特定系统的定制化努力，但DLKoopman是一个通用工具，可应用于任何动力系统的数据驱动学习和优化。它既可以在系统单个状态（快照）的数据上训练，用于预测其未知状态，也可以在系统轨迹的数据上训练，用于预测新初始状态的未知轨迹。DLKoopman可在Python包索引（PyPI）上以'dlkoopman'名称获取，并包含大量文档和教程。该软件包的其他贡献包括一个用于评估性能的新指标，称为平均归一化绝对误差，以及一个用于提高性能的即用型超参数搜索模块。
英文摘要：
We present DLKoopman -- a software package for Koopman theory that uses deep learning to learn an encoding of a nonlinear dynamical system into a linear space, while simultaneously learning the linear dynamics. While several previous efforts have either restricted the ability to learn encodings, or been bespoke efforts designed for specific systems, DLKoopman is a generalized tool that can be applied to data-driven learning and optimization of any dynamical system. It can either be trained on data from individual states (snapshots) of a system and used to predict its unknown states, or trained on data from trajectories of a system and used to predict unknown trajectories for new initial states. DLKoopman is available on the Python Package Index (PyPI) as 'dlkoopman', and includes extensive documentation and tutorials. Additional contributions of the package include a novel metric called Average Normalized Absolute Error for evaluating performance, and a ready-to-use hyperparameter search module for improving performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
