12 papers
NuclearDiffusion: Text-to-Image Foundation Models for Learning Nuclear Energy Concepts
Mohammed I. Radaideh, Jeremy Moon, Andre Gala-Garza +3
Generative artificial intelligence (AI) has transformed text-to-image synthesis, yet its ability to represent specialized engineering domains remains largely unexplored. As an exma…
Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks
Nataly R. Panczyk, Athanasios Stamatopoulos, Josef Svoboda +1
This work investigates the feasibility of augmenting traditional R-Matrix codes with a robust machine learning framework for automatically detecting neutron resonances in transmiss…
High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
Logan A. Burnett, Hyungjun Kim, Hsien-Cheng Chou +5
This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a four-loop pressurized water r…
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
Umme Mahbuba Nabila, Paul Seurin, Linyu Lin +1
Real-time supervisory control of thermal energy distribution systems requires digital twins that are accurate, interpretable, and uncertainty-aware, yet remain data and computation…
MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
Omer F. Erdem, Dean Price, Paul Seurin +1
Multiobjective optimization remains challenging for many scientific and engineering problems due to the need to balance convergence, diversity, and computational efficiency across…
Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors
Meredith Eaheart, Majdi I. Radaideh
High-fidelity computational fluid dynamics (CFD) simulations are widely used to analyze nuclear reactor transients, but are computationally expensive when exploring large parameter…