Ph.D. student in physics at Massachusetts Institute of Technology.
As physicists build ever more advanced particle accelerators, corresponding simulation softwares demand more computational resources. Our experiment, IsoDAR, is no exception to this. To reduce computational overhead of high-fidelity simulations, we used Julia to develop machine learning models that can, with reasonable accuracy, predict the behavior of a beam traversing our accelerator. These surrogate models have the potential to transform the way physicists design and optimize accelerators.