Machine learning for design optimization of storage ring nonlinear dynamics

TL;DR

This paper introduces a neural network-based surrogate modeling approach to accelerate the multi-objective optimization of nonlinear beam dynamics in storage rings, demonstrated on SPEAR3, outperforming traditional algorithms.

Contribution

The study presents a novel neural network surrogate model method for faster, more efficient storage ring nonlinear dynamics optimization, improving convergence speed over genetic and particle swarm algorithms.

Findings

Faster convergence to Pareto front compared to genetic algorithms.

Effective optimization of dynamic and momentum apertures in SPEAR3.

Neural network surrogate accelerates storage ring design optimization.

Abstract

A novel approach to expedite design optimization of nonlinear beam dynamics in storage rings is proposed and demonstrated in this study. At each iteration, a neural network surrogate model is used to suggest new trial solutions in a multi-objective optimization task. The surrogate model is then updated with the new solutions, and this process is repeated until the final optimized solution is obtained. We apply this approach to optimize the nonlinear beam dynamics of the SPEAR3 storage ring, where sextupole knobs are adjusted to simultaneously improve the dynamic aperture and the momentum aperture. The approach is shown to converge to the Pareto front considerably faster than the genetic and particle swarm algorithms.