Speaker
Description
Fast simulations of intense relativistic electron beams can be sufficiently accurate to allow for tuning of an accelerator’s magnetic transport field, but are incapable of capturing all relevant beam physics due to limitations in the model. Because methods that do capture these effects are significantly more computationally-expensive, e.g. particle in-cell simulations, they are fundamentally less useful for optimization problems. Here, transfer learning with high-fidelity, full-physics models is applied to the output of a machine learning model trained on a dataset generated by a fast particle beam simulation to bring the simulation results more in line with experimental data.
This work was done by Mission Support and Test Services, LLC, under Contract No. DE-NA0003624 with the U.S. Department of Energy, and the National Nuclear Security Administration. DOE/NV/03624--2062.
