Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations
This work addresses EMI/EMC simulation challenges for engineers by proposing a potentially more efficient AI-based approach, though it appears incremental as it builds on existing PINN and KAN methods.
The paper investigates using Kolmogorov-Arnold Networks (KANs) within Physics-Informed Neural Networks (PINNs) to simulate Electromagnetic Interference (EMI) problems, aiming to replace complex full-wave numerical simulations with AI-driven solutions for reduced energy consumption and computational demands.
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.