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

Kun Qian, Mohamed Kheir
5/18/2024
cs.LG

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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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Cite this paper

@article{qian2024investigating,
  title  = {Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations},
  author = {Kun Qian and Mohamed Kheir},
  year   = {2024},
  eprint = {2405.11383},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.11383v2}
}

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