Physics-Informed Neural Networks for the safety analysis of nuclear reactors

2025/07/01 by Piero Baraldi, Camilla Battistini, Federico Antonello +2 · 14 citations

paper · doi:10.1016/j.pnucene.2025.105745

published in Progress in Nuclear Energy 185, 105745 (Elsevier BV)

crossref created 2025/03/17 · crossref issued 2025/07/01 · crossref published 2025/07/01 · crossref published-print 2025/07/01 · crossref deposited 2025/12/29 · crossref indexed 2026/08/06

Citations

• Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms
• Probabilistic neural networks for fluid flow surrogate modeling and data recovery
• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
• A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
• The Application of Reactor Kinetics to the Analysis of Experiments

Cited by

• Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module