论文标题
DLKOOPMAN：库普曼理论的深度学习软件包
DLKoopman: A deep learning software package for Koopman theory
论文作者
论文摘要
我们提出了DLKoopman- Koopman理论的软件包，该软件包使用深度学习将非线性动力学系统的编码学习到线性空间中，同时学习线性动力学。尽管以前的几项努力要么限制了学习编码的能力，要么是为特定系统设计的定制工作，但DLKoopman是一种通用工具，可以应用于数据驱动的学习和对任何动态系统的优化。它可以对系统的数据（快照）的数据进行培训，并用于预测其未知状态，或者对系统轨迹的数据进行了培训，并用于预测新初始状态的未知轨迹。 DLKOOPMAN可在Python软件包索引（PYPI）上以“ DLKOOPMAN”的形式提供，并包含大量文档和教程。包装的其他贡献包括一个新颖的度量标准，称为评估性能的平均正常化绝对错误，以及一个可用的超参数搜索模块以改善性能。
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.
