  Particle-in-cell simulations are among the most essential tools for the
modeling and optimization of laser-plasma accelerators, since they reproduce
the physics from first principles. However, the high computational cost
associated with them can severely limit the scope of parameter and design
optimization studies. Here, we show that a multitask Bayesian optimization
algorithm can be used to mitigate the need for such high-fidelity simulations
by incorporating information from inexpensive evaluations of reduced physical
models. In a proof-of-principle study, where a high-fidelity optimization with
FBPIC is assisted by reduced-model simulations with Wake-T, the algorithm
demonstrates an order-of-magnitude speedup. This opens a path for the
cost-effective optimization of laser-plasma accelerators in large parameter
spaces, an important step towards fulfilling the high beam quality requirements
of future applications.
