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

• URL: http://arxiv.org/abs/2405.11383v2
• Date: Tue, 21 May 2024 11:00:13 GMT
• Title: Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations
• Authors: Kun Qian, Mohamed Kheir,
• Abstract summary: It introduces some common EM problem formulations and how they can be solved using AI-driven solutions. This research may open new horizons for green EMI simulation with less energy consumption and feasible computational capacity.
• Score: 3.2005061268752746
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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