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Particle-in-cell simulations are among the most essential tools for themodeling and optimization of laser-plasma accelerators, since they reproducethe physics from first principles. However, the high computational costassociated with them can severely limit the scope of parameter and designoptimization studies. Here, we show that a multitask Bayesian optimizationalgorithm can be used to mitigate the need for such high-fidelity simulationsby incorporating information from inexpensive evaluations of reduced physicalmodels. In a proof-of-principle study, where a high-fidelity optimization withFBPIC is assisted by reduced-model simulations with Wake-T, the algorithmdemonstrates an order-of-magnitude speedup. This opens a path for thecost-effective optimization of laser-plasma accelerators in large parameterspaces, an important step towards fulfilling the high beam quality requirementsof future applications.

Bayesian optimization of laser-plasma accelerators assisted by reduced physical models