Dean Price

Gradient-based optimization of nuclear criticality experiments using neural surrogate eigenvalue sensitivities

A physics-informed deep neural network is trained to predict the neutronic sensitivity of grid-based critical experiment geometries and the differentiability of the neural network is used to enable gradient-based design optimization of new experiment geometries to maximize the sensitivity profile of a target technology...