Bayesian multi-task optimization of laser-plasma accelerators using Particle-In-Cell codes with different fidelities

When designing a laser-plasma acceleration setup, it is common to explore the parameter space (plasma density, laser intensity, focal position, etc.) with Particle-In-Cell (PIC) simulations in order to find an optimal configuration that, for example, minimizes the energy spread or emittance of the accelerated beam. However, laser-plasma acceleration is typically modeled with full PIC codes, which can be computationally expensive. Various reduced models can approximate beam behavior at a much lower computational cost. Although such models do not capture the full physics, they could still suggest promising sets of parameters to be simulated with a full PIC code and thereby speed up the overall design optimization.

In this work, we automate such a workflow with a Bayesian multitask algorithm, where the different tasks correspond to PIC codes making different approximations. Thus, this algorithm learns from past simulation results from both full PIC codes and reduced PIC codes and dynamically chooses the next parameters to be simulated. We illustrate this workflow with a proof-of-concept optimization using the Wake-T and FBPIC codes. The libEnsemble library is used to orchestrate this workflow on a modern GPU-accelerated high-performance computing system.

This research was supported in part by the U.S. Department of Energy, Office of Science, under contract numbers DE-AC02-06CH11357 and DE-AC02-05CH11231

and by the Exascale Computing Project (17-SC-20-SC). This research was supported in part through the Maxwell computational resources operated at DESY, Hamburg, Germany.