PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918105662357504 |
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| author | Ye, David Williams, Jan Gao, Mars Riva, Stefano Tomasetto, Matteo Zoro, David Kutz, J. Nathan |
| author_facet | Ye, David Williams, Jan Gao, Mars Riva, Stefano Tomasetto, Matteo Zoro, David Kutz, J. Nathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20954 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery Ye, David Williams, Jan Gao, Mars Riva, Stefano Tomasetto, Matteo Zoro, David Kutz, J. Nathan Machine Learning Computational Engineering, Finance, and Science Dynamical Systems Chaotic Dynamics 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. |
| title | PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery |
| topic | Machine Learning Computational Engineering, Finance, and Science Dynamical Systems Chaotic Dynamics |
| url | https://arxiv.org/abs/2507.20954 |