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High-fidelity physics simulations are powerful tools in the design andoptimization of charged particle accelerators. However, the computationalburden of these simulations often limits their use in practice for designoptimization and experiment planning. It also precludes their use as onlinemodels tied directly to accelerator operation. We introduce an approach basedon machine learning to create nonlinear, fast-executing surrogate models thatare informed by a sparse sampling of the physics simulation. The models are10^6 to 10^7 times more efficient to execute.We also demonstrate that thesemodels can be reliably used with multi-objective optimization to obtainorders-of-magnitude speedup in initial design studies and experiment planning.For example, we required 132 times fewer simulation evaluations to obtain anequivalent solution for our main test case, and initial studies suggest thatbetween 330 to 550 times fewer simulation evaluations are needed when using aniterative retraining process. Our approach enables new ways for high-fidelityparticle accelerator simulations to be used, at comparatively littlecomputational cost.

Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems