Further investigation of convolutional neural networks applied in computational electromagnetism under physics‐informed consideration

Author:

Gong Ruohan1,Tang Zuqi1ORCID

Affiliation:

1. Univ. Lille, Arts et Metiers Institute of Technology Centrale Lille Junia ULR 2697‐L2EP Lille France

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Physics-informed neural network for engineers: a review from an implementation aspect;Journal of Mechanical Science and Technology;2024-07

2. Transfer learning‐based physics‐informed neural networks for magnetostatic field simulation with domain variations;International Journal of Numerical Modelling: Electronic Networks, Devices and Fields;2024-07

3. Hybrid Boundary Element – Physics Informed Neural Network Formulation for Electromagnetics Problems;2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC);2024-06-02

4. Electromagnetic Scattering of Infinitely Long Cylinder of Arbitrary Cross-section Based on PINNs;2024 Photonics & Electromagnetics Research Symposium (PIERS);2024-04-21

5. Inverse Scattering Solver Based on Deep Neural Network With Total Variation Regularization;IEEE Antennas and Wireless Propagation Letters;2023-10

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