Physics-informed neural network method for modelling beam-wall interactions

Abstract

A mesh-free approach for modelling beam-wall interactions in particle accelerators is proposed. The key idea of our method is to use a deep neural network as a surrogate for the solution to a set of partial differential equations involving the particle beam, and the surface impedance concept. The proposed approach is applied to the coupling impedance of an infinitely long vacuum chamber with a thin conductive coating, and also verified in comparison with traditional numerical methods.