Shallow Recurrent Decoder for Nuclear Reactors Applications (NuSHRED)
This repository collects the codes regarding the application of the Shallow REcurrent Decoder (SHRED) method to Nuclear Reactors systems 🏭⚛️
📄 Related Publications
This repository serves as complementary code to the following papers:
[P1] Riva, S., Introini, C., Cammi, A., & Kutz, J. N. (2024). Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors. arXiv [Physics.Ins-Det].
[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
[P3] Introini, C., Riva, S., Kutz, J. N. & Cammi, A.(2025). From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
📊 Simulation Data
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
🎥 If you want to know more about the SHRED method for nuclear reactors, check out this YouTube video!
🏗️ Foundations of SHRED
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]
📌 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]
📌 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: - Riva et al. (2024) - Cammi et al. (2024)
📂 Repository Structure
📁 shred/ → Modules for the implementation of the SHRED network from github.com/Jan-Williams/pyshred and github.com/MatteoTomasetto/SHRED-ROM
📁 Code/ → Subfolders corresponding to the applications of SHRED in nuclear reactor concepts, with datasets associated as follows:
| MSFR-ULOFF D1 | MSFR-ULOFF D2 | DYNASTY D3 | |
|---|---|---|---|
| P1 | ✅ | ||
| P2 | ✅ | ||
| P3 | ✅ |
▶️ How to Execute
1️⃣ Clone or download the repository.
2️⃣ Download the datasets and move them into the appropriate directory.
3️⃣ 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.
📬 Contact Information
For inquiries, please contact: 📧 stefano.riva@polimi.it, carolina.introini@polimi.it, antonio.cammi@polimi.it, kutz@uw.edu.
For issues or bugs, refer to the GitHub Issues section of this repository.
📊 Results
📌 Paper 1
| Fast Flux ϕ1 | Temperature T | Velocity u |
|---|---|---|
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📌 Paper 2
Out-Core Sensing (Fast Flux)
| Fast Flux ϕ1 | Temperature T | Velocity u | Precursors Group 1 c1 |
|---|---|---|---|
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Mobile Sensors (Fist Group of Precursors)
| Fast Flux ϕ1 | Temperature T | Velocity u | Precursors Group 1 c1 |
|---|---|---|---|
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Mobile Probes (only position measaured)
| Fast Flux ϕ1 | Temperature T | Velocity u | Precursors Group 1 c1 |
|---|---|---|---|
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📌 Paper 3
| Case | Visualization |
|---|---|
| Parametric Verification |  |
| —————————– | ————————————————————- |
| Parametric Validation |  |
| Prediction Validation |  |