Deep-Learning-Equipped Iterative Solution of Electromagnetic Scattering From Dielectric Objects

Abstract

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.