Physics-Informed Neural Networks for Solving 2-D Magnetostatic Fields
Author:
Affiliation:
1. Beijing Research Institute, Zhejiang Lab, Beijing, China
2. EEE, Imperial College London, London, U.K
3. College of Electrical Engineering, Zhejiang University, Hangzhou, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials
Link
http://xplorestaging.ieee.org/ielx7/20/10294220/10141630.pdf?arnumber=10141630
Reference15 articles.
1. Magnetostatics and micromagnetics with physics informed neural networks
2. Physics Informed Neural Networks for Electromagnetic Analysis
3. DeepXDE: A Deep Learning Library for Solving Differential Equations
4. Physics-Informed Neural Networks with Hard Constraints for Inverse Design
5. Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems
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3. 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
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