Abstract
While machine learning is revolutionizing every corner of modern technologies, we have been attempting to explore whether machine learning methods could be used in computational electromagnetic (CEM). In this paper, five efforts in line with this direction are reviewed. They include forward methods such as the method of moments (MoM) solved by the artificial neural network training process, FDTD PML (perfectly matched layer) using the hyperbolic tangent basis function (HTBF), etc. There are also inverse problems that use the deep ConvNets for the effective source reconstruction and subwavelength imaging in the far-field. Benchmarks are provided to demonstrate the feasibility of all explorations. The reviewed works are attempting to open the new path for employing machine learning in the modern CEM.
Recommended Citation
H. M. Yao et al., "Machine Learning Methodology Review For Computational Electromagnetics," 2019 International Applied Computational Electromagnetics Society Symposium-China, ACES 2019, article no. 9060439, Institute of Electrical and Electronics Engineers, Aug 2019.
The definitive version is available at https://doi.org/10.23919/ACES48530.2019.9060439
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
ANN; CEM; ConvNet; Deep learning; EM inverse; FDTD; Machine learning; MoM; PML; Source reconstruction method; Subwavelength imaging
International Standard Book Number (ISBN)
978-099600789-4
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Aug 2019
Comments
National Natural Science Foundation of China, Grant FA2386-17-1-0010