[2511.16148] Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models

Computer Science > Machine Learning arXiv:2511.16148 (cs) [Submitted on 20 Nov 2025 (v1), last revised 25 Mar 2026 (this version, v3)] Title:Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models Authors:Perceval Beja-Battais (CB), Alain Grossetête, Nicolas Vayatis (CB) View a PDF of the paper titled Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models, by Perceval Beja-Battais (CB) and 2 other authors View PDF Abstract:In recent years, there has been an increasing need for Nuclear Power Plants (NPPs) to improve flexibility in order to match the rapid growth of renewable energies. The Operator Assistance Predictive System (OAPS) developed by Framatome addresses this problem through Model Predictive Control (MPC). In this work, we aim to improve MPC methods through data-driven simulation schemes. Thus, from a set of nonlinear stiff ordinary differential equations (ODEs), this paper introduces two surrogate models acting as alternative simulation schemes to enhance nuclear reactor core simulation. We show that both data-driven and physics-informed models can rapidly integrate complex dynamics, with a very low computational time (up to 1000x time reduction). Subjects: Machine Learning (cs.LG) Cite as: arXiv:2511.16148 [cs.LG]   (or arXiv:2511.16148v3 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2511.16148 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Perceval Beja-Battais [view email] [...