Deep Learning Equipped Iterative Solution of Electromagnetic Scattering From Dielectric Objects

Deep learning (DL) equipped iterators are developed to accelerate the iterative solution of electromagnetic scattering problems. In proposed iterators, DL blocks consisting of U-nets are employed to replace the nonlinear process of the traditional iterators, i.e., the conjugate gradient (CG) method...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2023-07, Vol.71 (7), p.1-1
Main Authors: Xue, Bo-Wen, Guo, Rui, Li, Mao-Kun, Sun, Sheng, Pan, Xiao-Min
Format: Article
Language:English
Subjects:
Computing time
Conjugate gradient method
Deep learning
Electromagnetic scattering
Electromagnetics
Integral equations
Iterative methods
Iterative solution
Iterative Solvers
Method of moments
Time-domain analysis
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Summary:Deep learning (DL) equipped iterators are developed to accelerate the iterative solution of electromagnetic scattering problems. In proposed iterators, DL blocks consisting of U-nets are employed to replace the nonlinear process of the traditional iterators, i.e., the conjugate gradient (CG) method and generalized minimal residual (GMRES) method. New implementations of the complex-valued batch normalization in the U-net are proposed and investigated in terms of the DL equipped iterators. Numerical results show that the DL equipped iterators outperforms their traditional counterparts in terms of computational time under the comparable accuracy since the phase information of the currents, fields and permittivity are properly handled.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2023.3264701