Physics Informed Neural Networks for Electromagnetic Analysis

• Published in: IEEE transactions on magnetics Vol. 58; no. 9; pp. 1 - 4
• Main Authors: Khan, Arbaaz, Lowther, David A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Boundary conditions; Computational electromagnetics; Deep learning; Electrostatics; Feasibility studies; Finite element method; Magnetic domains; Magnetism; Magnetostatics; Mathematical analysis; Mathematical models; Neural networks; neural networks (NNs); numerical analysis; Partial differential equations; partial differential equations (PDEs); Training

Deep learning has achieved remarkable success in diverse applications; however, its use in solving partial differential equations (PDEs) has emerged only recently. Here, we present a feasibility study of applying physics-informed deep learning methods for solving PDEs related to the physical laws of...