Stochastic Gradient Descent for Optimization of Nuclear Systems

Abstract

The use of gradient descent methods for optimizing k-eigenvalue nuclear system has been shown to be useful in the past, but the k-eigenvalue gradients have proved challenging due to their stochastic nature and uncertainty. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue systems. ADAM is able to successfully optimize nuclear systems using the gradients of k-eigenvalue problems despite their stochastic nature and uncertainty.