PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery

TL;DR

PySHRED is a Python package that implements the SHallow Recurrent Decoding strategy for modeling complex dynamical systems, enabling robust sensing, model reduction, and scientific discovery from high-dimensional, noisy, and nonlinear data.

Contribution

It introduces PySHRED v1.0, a comprehensive, modular Python package with advanced methods for handling real-world dynamical data in scientific applications.

Findings

Supports noisy, multi-scale, and high-dimensional data

Includes robust sensing and model reduction extensions

Facilitates scientific discovery from complex datasets

Abstract

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.