PyDMD：一个用于鲁棒动态模态分解的Python包
PyDMD: A Python package for robust dynamic mode decomposition
AI总结：
本文介绍了PyDMD包，它实现了动态模态分解（DMD）及其多种变体，扩展了处理噪声、多尺度、高维和强非线性动力学的前沿方法，为动力系统分析提供了完整工具。
摘要：
动态模态分解（DMD）是一种简单而强大的数据驱动建模技术，能够从数据中揭示相干的时空模式。该方法基于线性代数的表述还允许进行各种优化和扩展，使该算法在真实世界数据分析中实用且可行。因此，DMD已发展成为跨多个科学学科进行动力系统分析的主导方法。PyDMD是一个实现DMD及其几个主要变体的Python包。在这项工作中，我们扩展了PyDMD包，纳入了众多前沿的DMD方法和工具，专门设计用于处理噪声、多尺度、参数化、维度过高甚至强非线性的动力学。我们提供了截至1.0版本PyDMD可用功能的完整概述，以及对DMD算法理论的简要介绍、面向开发者的信息、关于DMD实际使用的建议和入门代码示例。所有代码均可在https://github.com/PyDMD/PyDMD获取。
英文摘要：
The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's linear algebra-based formulation additionally allows for a variety of optimizations and extensions that make the algorithm practical and viable for real-world data analysis. As a result, DMD has grown to become a leading method for dynamical system analysis across multiple scientific disciplines. PyDMD is a Python package that implements DMD and several of its major variants. In this work, we expand the PyDMD package to include a number of cutting-edge DMD methods and tools specifically designed to handle dynamics that are noisy, multiscale, parameterized, prohibitively high-dimensional, or even strongly nonlinear. We provide a complete overview of the features available in PyDMD as of version 1.0, along with a brief overview of the theory behind the DMD algorithm, information for developers, tips regarding practical DMD usage, and introductory coding examples. All code is available at https://github.com/PyDMD/PyDMD .
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。
