pyshred 1.0.21

PySHRED: Package for Shallow Recurrent Decoding

PySHRED

PySHRED is a deep-learning library for reconstructing and forecasting high-dimensional spatiotemporal systems from sparse sensor data.

Built on the SHallow REcurrent Decoder (SHRED) architecture, PySHRED provides a seamless pipeline from raw sensor measurements to high-fidelity reconstructions and long-horizon forecasts.

SHRED in a Nutshell

Component	Role	Models
Sequence model	Encodes temporal sensor measurements into a low-dimensional latent state.	LSTM, GRU, Transformer
Decoder model	Reconstructs the full high-dimensional state from the latent state.	MLP, U-Net
Latent forecaster	Propagates latent dynamics forward in time for long-horizon prediction.	LSTM, SINDy

The sequence + decoder pair reconstructs the full high-dimensional state space from sparse sensors, while the forecaster + decoder pair enables multi-step forecasting with no additional sensor measurements.

PySHRED is a powerful tool for:

• System identification
• Reduced-order modeling
• Long-horizon forecasting
• Latent dynamics discovery
• Parametric systems analysis
• Control and decision-making

PySHRED offers a high-level interface and a simple three-step pipeline, making it easy for anyone to get started.

Documentation

Online documentation: pyshred-dev.github.io/pyshred/stable

The docs include:

• 📘 Getting Started
• 📖 User Guide
• 🧪 Example Gallery
• 🛠️ API Reference

Installation

• Installing from PyPI

  The latest stable release (and required dependencies) can be installed from PyPI:

  pip install pyshred
  

• Installing from source

  PySHRED can be installed via source code on GitHub.

  git clone https://github.com/pyshred-dev/pyshred.git
  cd pyshred
  pip install .
  

Citing

Citation instructions coming soon.

Resources

• Docs: https://pyshred-dev.github.io/pyshred/stable
• Issue Tracking: https://github.com/pyshred-dev/pyshred/issues
• Source code: https://github.com/pyshred-dev/pyshred

Contributors and Developers

Nathan Kutz

Jan Williams

David Ye

Mars Gao

Matteo Tomasetto

Stefano Riva

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References

• Williams, J. P., Zahn, O., & Kutz, J. N. (2024).
  Sensing with shallow recurrent decoder networks.
  Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 480(2298), 20240054.

• Gao, M. L., Williams, J. P., & Kutz, J. N. (2025).
  Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks.
  arXiv preprint, arXiv:2501.13329.

• Tomasetto, M., Williams, J. P., Braghin, F., Manzoni, A., & Kutz, J. N. (2025).
  Reduced Order Modeling with Shallow Recurrent Decoder Networks.
  arXiv preprint, arXiv:2502.10930.

• Kutz, J. N., Reza, M., Faraji, F., & Knoll, A. (2024).
  Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics.
  arXiv preprint, arXiv:2405.11955.

• Ebers, M. R., Williams, J. P., Steele, K. M., & Kutz, J. N. (2024).
  Leveraging arbitrary mobile sensor trajectories with Shallow Recurrent Decoder Networks for full-state reconstruction.
  IEEE Access, 12, 97428–97439.