Inverse Critical Experiment Design via Gradient Optimization and a Multigroup Attention-Based Neural Network Architecture

This research presents a methodology for the inverse design of critical experiments, essential for validating advanced nuclear reactor designs. It employs deep neural network surrogate modeling and nonparametric gradient optimization to generate experiment geometries that maximize neutronic similarity.