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

David Ye, Jan Williams, Mars Gao, Stefano Riva, Matteo Tomasetto +2 more
7/28/2025
cs.LGcs.CEmath.DS

Official code: pyshred-dev/pyshred (37 stars, MIT, last commit 2025-06-25).

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.

View on arXivView PDF

Code Implementations(1)

pyshred-dev/pyshredOfficial100%
MIT

PySHRED: Package for Shallow Recurrent Decoding

375PythonMay 17, 20251 years agoMIT
data-drivendynamical-systemsforecastingpythonreconstruction+7 more

Similar Papers

T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders

81% match2025

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data

79% match2025

From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility

77% match1 repo132025

Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics

76% match1 repo112024

UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression

75% match2026

Recurrent Expansion: A Pathway Toward the Next Generation of Deep Learning

74% match2025

Cite this paper

@article{ye2025pyshred,
  title  = {PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery},
  author = {David Ye and Jan Williams and Mars Gao and Stefano Riva and Matteo Tomasetto and David Zoro and J. Nathan Kutz},
  year   = {2025},
  eprint = {2507.20954},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.20954v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2507.20954.svg)](https://codeofpaper.com/paper/2507.20954)

Discussion