arXiv Machine Learning
Real-Time Monitoring of MHD Liquid Metal Flows with Shallow Recurrent Decoders
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arXiv Machine Learning
Sep 7
Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
This paper presents a novel Shallow Recurrent Decoder network for efficient parametric state estimation in circulating fuel reactors, specifically applied to the Molten Salt Fast Reactor (MSFR). The model infers the full reactor state—including neutron fluxes, precursor concentrations, temperature, pressure, and velocity—using only three out‑of‑core neutron flux time‑series measurements, while also handling parametric time‑series data to explore different accident scenarios. The approach demonstrates accurate real‑time reconstruction with low training cost and provides uncertainty quantification, making it suitable for monitoring and control within a reactor digital twin.
arXiv Machine Learning
Sep 7
Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
The paper introduces Shallow Recurrent Decoder (SHRED) networks as a data‑driven method for accurate state estimation in engineering systems, specifically applied to the TRIGA Mark II research reactor. SHRED maps sparse sensor measurements to the full state space, handling noisy data and requiring minimal training time. The study demonstrates SHRED’s performance using both synthetic CFD data and experimental temperature recordings, achieving low reconstruction errors and showcasing its potential for real‑time monitoring and digital twin development.
arXiv Machine Learning
2d ago
PHASE: Multi-Regime Modeling of Incompressible Magnetohydrodynamics
arXiv:2609.37609v1 Announce Type: new Abstract: Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics...
arXiv Machine Learning
Jun 16
Towards Data-Efficient Cross-Device Generalization of Grad-Shafranov Equilibria via Transfer Learning Neural Operator
arXiv:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.
arXiv AI
Jun 12
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
arXiv:2602. 10132v3 Announce Type: replace-cross Abstract: Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors readings.
Hugging Face Trending Papers
Aug 24
AI Surrogate Modeling for Real-Time Tokamak Equilibrium Prediction: Benchmarking Neural Architectures and Validation on EXL-50U
Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solvers are often too costly for real-time deployment. We...