Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations

TL;DR

This paper explores the use of KAN-based Physics-Informed Neural Networks to improve electromagnetic interference simulations, aiming for more efficient and environmentally friendly computational methods.

Contribution

It introduces a novel application of KAN-based PINNs for EMI/EMC simulations, offering an alternative to traditional complex numerical methods.

Findings

KAN-based PINNs can effectively model EMI problems

Potential reduction in computational energy consumption

Feasibility demonstrated for AI-driven EMI simulations

Abstract

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.