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