Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915189974106112 |
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| author | Qian, Kun Kheir, Mohamed |
| author_facet | Qian, Kun Kheir, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations Qian, Kun Kheir, Mohamed Machine Learning 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. |
| title | Investigating KAN-Based Physics-Informed Neural Networks for EMI/EMC Simulations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.11383 |