mlsanityData to 7 Oct 2026

A Deep Learning-based surrogate model for Severe Accidents in nuclear reactors using ASTEC

arXiv 2607.04450 ↗First posted 5 Jul 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Integral codes like the Accident Source Term Evaluation Code (ASTEC) are powerful tools to study the physics of Severe Accidents (SAs) in nuclear reactors. Real time SA simulators can also be helpful in training operators of nuclear plants to react correctly to malfunctions. However, SA simulators can take up to several days per simulation, making their use infeasible for real time applications. In this work we show how to speed up a SA simulator with a fast, Deep Learning based (DL), surrogate model (SM). The SM is built as a combination of a dimensionality reduction stage, via an AutoEncoder, and a time-stepping stage, via a Neural Ordinary Differential Equation. The data on which the SM is trained are obtained from the ASTEC simulator, by sampling a set of operator actions for station blackout (SBO) and loss-of-coolant accidents (LOCA). The objective of the developed SM is to approximate multiple spatio-temporal fields for the thermal-hydraulic physics, core degradation, and fission product release modules in ASTEC's vessel domain. The SM predicts simultaneously around 80 different physical variables (both scalar and fields), maintaining a stable autoregressive rollout up to 50 thousand time steps. In addition, the AutoEncoder achieves a dimensionality reduction by a factor of over 300, which allows the SM to predict up to 40 hours of simulation in under a minute, both on CPU and GPU. This work is the first study of the capabilities and limits of DL based surrogate modeling in approximating the challenging, highly non-linear physics of ASTEC.

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References · 17

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5 more
  1. 2022Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifoldscited by 25
  2. 2024Space-Time Continuous PDE Forecasting using Equivariant Neural Fieldscited by 18
  3. 2023Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networkscited by 17
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  5. 2025A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEscited by 3

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