Electromagnetic Modeling Using an FDTD-Equivalent Recurrent Convolution Neural Network: Accurate Computing on a Deep Learning Framework

In this study, a recurrent convolutional neural network (RCNN) is designed for full-wave electromagnetic (EM) modeling. This network is equivalent to the finite difference time domain (FDTD) method. The convolutional kernel can describe the finite difference operator, and the recurrent neural networ...

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Bibliographic Details
Published in:IEEE antennas & propagation magazine 2023-02, Vol.65 (1), p.2-11
Main Authors: Guo, Liangshuai, Li, Maokun, Xu, Shenheng, Yang, Fan, Liu, Li
Format: Magazinearticle
Language:English
Subjects:
Artificial neural networks
Computational modeling
Convolution
Deep learning
Equivalence
Finite difference methods
Finite difference time domain method
Finite differences
Mathematical analysis
Mathematical models
Modelling
Operators (mathematics)
Recurrent neural networks
Time-domain analysis
Training
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Summary:In this study, a recurrent convolutional neural network (RCNN) is designed for full-wave electromagnetic (EM) modeling. This network is equivalent to the finite difference time domain (FDTD) method. The convolutional kernel can describe the finite difference operator, and the recurrent neural network (RNN) provides a framework for the time-marching scheme in FDTD. The network weights are derived from the FDTD formulation, and the training process is not needed. Therefore, this FDTD-RCNN can rigorously solve a given EM modeling problem as an FDTD solver does.
ISSN:1045-9243
1558-4143
DOI:10.1109/MAP.2021.3127514