Summary of Investigating Kan-based Physics-informed Neural Networks For Emi/emc Simulations, by Kun Qian and Mohamed Kheir
Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations
by Kun Qian, Mohamed Kheir
First submitted to arxiv on: 18 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to electromagnetic interference (EMI) simulations is proposed in this paper, leveraging Physics-Informed Neural Networks (PINNs), specifically KolmogorovArnold Networks (KANs). The study investigates the feasibility of using AI-driven solutions for EMI simulations, potentially reducing energy consumption and computational complexity. The research introduces common EM problem formulations and explores their solution using PINNs-based methods instead of traditional full-wave numerical simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to do electromagnetic interference (EMI) simulations using special kinds of artificial intelligence called Physics-Informed Neural Networks (PINNs). Instead of doing complex computer simulations, this method can help reduce energy consumption and make EMI simulation faster. The study looks at how different EMI problems can be solved in this way. |
