PySHRED工具包让复杂系统建模更高效，适合处理噪声和高维数据。
PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
- 基于浅层循环解码器，从观测数据中学习动态系统演化规律
- 支持降维、物理规律发现，对噪声、多尺度数据有强鲁棒性
- 开源易用，适用于科研人员快速构建复杂系统的数据驱动模型
浅层循环解码器（SHRED）是一种深度学习方法，用于从动力系统快照观测中建模高维动态系统及时空数据。本文介绍PySHRED 1.0版本，该工具包实现了SHRED及其多项重要扩展，包括鲁棒传感、降阶建模和物理规律发现。其包含数据预处理器和多个前沿SHRED方法，专为处理真实世界数据而设计——这些数据可能含有噪声、具有多尺度特征、参数化、维度极高且高度非线性。工具包安装简便、文档完整，提供大量代码示例，结构模块化，便于未来扩展。整个代码库以MIT许可证发布，开源地址为https://github.com/pyshred-dev/pyshred。
原文摘要 · Abstract (English)
SHallow REcurrent Decoders (SHRED) provide a deep learning strategy for modeling high-dimensional dynamical systems and/or spatiotemporal data from dynamical system snapshot observations. PySHRED is a Python package that implements SHRED and several of its major extensions, including for robust sensing, reduced order modeling and physics discovery. In this paper, we introduce the version 1.0 release of PySHRED, which includes data preprocessors and a number of cutting-edge SHRED methods specifically designed to handle real-world data that may be noisy, multi-scale, parameterized, prohibitively high-dimensional, and strongly nonlinear. The package is easy to install, thoroughly-documented, supplemented with extensive code examples, and modularly-structured to support future additions. The entire codebase is released under the MIT license and is available at https://github.com/pyshred-dev/pyshred.
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