PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
This provides a software tool for researchers and practitioners in scientific computing and machine learning to apply SHRED methods to complex dynamical systems, but it is incremental as it packages existing methods.
The authors introduced PySHRED, a Python package implementing SHallow REcurrent Decoders (SHRED) for modeling high-dimensional dynamical systems from sparse data, designed to handle noisy, multi-scale, and nonlinear real-world data with features like robust sensing and physics discovery.
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.