Speaker
Francesco Maria Velotti
(CERN)
Description
Most of limitations to fully exploit the CERN injector complex comes from problems that have a rather known formalism, but usually, due to interplay of many other factors make the analytical or numerical solutions almost impossible. In this context, machine learning is playing a key role allowing us to produce empirical models to estimate the accelerator behaviour to the desired accuracy for its control and optimisation. In some cases though, data availability is limited or there is the need to extrapolate beyond the training domain. In this context, Physics Informed Machine Learning may hold the key more powerful models. This talk will introduce some of the ongoing work and highlight possible paths for the future.
