  The main objective of this paper is to investigate the feasibility of
employing Physics-Informed Neural Networks (PINNs) techniques, in particular
KolmogorovArnold Networks (KANs), for facilitating Electromagnetic Interference
(EMI) simulations. It introduces some common EM problem formulations and how
they can be solved using AI-driven solutions instead of lengthy and complex
full-wave numerical simulations. This research may open new horizons for green
EMI simulation workflows with less energy consumption and feasible
computational capacity.
