Implementing the Fast Full Wave Electromagnetic Forward Solver Using the Deep Convolutional Encoder-Decoder Architecture

In this paper, a novel deep learning (DL) based solver is proposed for the electromagnetic forward (EMF) process. It is based on the complex-valued deep convolutional neural networks (DConvNets) comprising an encoder network and a corresponding decoder network with pixel-wise regression layer. The e...

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Bibliographic Details
Published in:	IEEE transactions on antennas and propagation Vol. 71; no. 1; p. 1
Main Authors:	Yao, He Ming, Jiang, Lijun, Ng, Michael
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Coders Computational modeling Convolutional Neural Network Decoding Deep Learning Electromagnetic Forward Process Electromagnetics Encoders-Decoders Mathematical models Real Time Real-time systems Solvers Three-dimensional displays Training Transformations (mathematics)
ISSN:	0018-926X, 1558-2221
Online Access:	Get full text
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