This repository collects the codes regarding the application of the Shallow REcurrent Decoder (SHRED) method to Nuclear Reactors systems πβοΈ
This repository serves as complementary code to the following papers:
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[P1] Riva, S., Introini, C., Cammi, A., & Kutz, J. N. (2025). Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors. Progress in Nuclear Energy, vol. 189, pp. 105928
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[P2] Riva, S., Introini, C., Kutz, J. N. & Cammi, A. (2025). Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
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[P3] Riva, S., Missaglia A., Introini, C., Kutz, J. N. & Cammi, A.(2026). From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
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[P4] Riva, S., Introini, C., Cammi, A., & Kutz, J. N. (2025). Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor.
Upcoming works: 2 contributions will be presented at the PHYSOR2026 conference, two preprints on arxiv have been submitted on the application of SHRED to Fusion MHD systems (code will be released soon).
The compressed simulation datasets are available on Zenodo:
- [D1] Molten Salt Fast Reactor (MSFR) in the accidental scenario Unprotected Loss Of Fuel Flow (ULOFF) - Single Transient (Reconstruction mode)
- [D2] Molten Salt Fast Reactor (MSFR) in the accidental scenario Unprotected Loss Of Fuel Flow (ULOFF) - Parametric Transients
- [D3] DYNASTY Experimental Facility - Single Transient (Reconstruction & Prediction mode) and Parametric Transients
- [D4] CFD model of TRIGA Mark II Reactor - Single Transient (Reconstruction mode)
π₯ If you want to know more about the SHRED method for nuclear reactors, check out this YouTube video!
The SHRED method was first proposed and developed in this paper:
- J. Williams, O. Zahn and J. N. Kutz, Sensing with shallow recurrent decoder networks, arXiv (2023) [arXiv:2301.12011]
π The original code base is available here: github.com/Jan-Williams/pyshred
This repository also builds upon a related implementation:
- Matteo Tomasetto, Jan P. Williams, Francesco Braghin, Andrea Manzoni, J. Nathan Kutz, Reduced Order Modeling with Shallow Recurrent Decoder Networks, arXiv (2025) [arXiv:2502.10930]
π Improvements for Parametric datasets are available here: github.com/MatteoTomasetto/SHRED-ROM
Additionally, the pyforce package is used for sensor placements and EIM/GEIM comparison in P1. See:
π shred/ β Modules for the implementation of the SHRED network from github.com/Jan-Williams/pyshred and github.com/MatteoTomasetto/SHRED-ROM
π Code/ β Subfolders corresponding to the applications of SHRED in nuclear reactor concepts, with datasets associated as follows:
| MSFR-ULOFF D1 | MSFR-ULOFF D2 | DYNASTY D3 | TRIGA D4 | |
|---|---|---|---|---|
| P1 | β | |||
| P2 | β | |||
| P3 | β | |||
| P4 | β |
1οΈβ£ Clone or download the repository.
2οΈβ£ Download the datasets and move them into the appropriate directory.
3οΈβ£ Install the required dependencies:
- Main dependencies: pytorch, numpy, scikit-learn, matplotlib, scipy, pyvista.
- For P1, pyforce is required, see installation instructions
Other packages will be installed as part of the requirements.
For inquiries, please contact: π§ [email protected], [email protected], [email protected], [email protected].
For issues or bugs, refer to the GitHub Issues section of this repository.
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Out-Core Sensing (Fast Flux)
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Precursors Group 1 |
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Mobile Sensors (Fist Group of Precursors)
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Mobile Probes (only position measaured)
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| Case | Visualization |
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| Parametric Verification | ![]() |
| Parametric Validation | ![]() |
| Prediction Validation | ![]() |
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