A U-Net surrogate with multigroup attention pooling is trained on OpenMC sensitivity data and combined with gradient optimization to generate grid-based critical experiment geometries that achieve c_k values up to 0.97757 for HALEU fuel validation.
GeN-Foam model and benchmark of delayed neutron precursor drift in the Molten Salt Reactor Experiment
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Residence-time theory combined with DNP transport yields closed-form static reactivity loss and zero-power transfer function for CFRs, generalizing plug-flow and CSTR cases via a mixing parameter and validated on MSRE data plus Serpent-2/CFD results.
Generative models including VAEs, normalizing flows, GANs, and diffusion models can learn neutron source distributions from Monte Carlo lists for fast, memory-free sampling after training.
citing papers explorer
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Inverse Critical Experiment Design via Gradient Optimization and a Multigroup Attention-Based Neural Network Architecture
A U-Net surrogate with multigroup attention pooling is trained on OpenMC sensitivity data and combined with gradient optimization to generate grid-based critical experiment geometries that achieve c_k values up to 0.97757 for HALEU fuel validation.
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Residence-time theory applied to circulating-fuel reactors: zero-power analysis
Residence-time theory combined with DNP transport yields closed-form static reactivity loss and zero-power transfer function for CFRs, generalizing plug-flow and CSTR cases via a mixing parameter and validated on MSRE data plus Serpent-2/CFD results.
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Machine Learning for neutron source distributions
Generative models including VAEs, normalizing flows, GANs, and diffusion models can learn neutron source distributions from Monte Carlo lists for fast, memory-free sampling after training.