A Deep Learning Scheme of Electromagnetic Scattering From Scatterers With Incomplete Profiles
Ji-Yuan Wang, Xin-Yue Lou, Liang Zhang, Yun-Chuan Wang, Xiao-Min Pan

TL;DR
This paper introduces a deep learning approach to solve electromagnetic scattering problems involving incomplete dielectric profiles, effectively integrating forward and inverse problems to improve accuracy with limited data.
Contribution
It presents a novel deep learning scheme that simultaneously addresses forward and inverse EM scattering problems with incomplete profile data, a challenge not effectively handled by existing solvers.
Findings
The scheme successfully reconstructs dielectric profiles from limited scattering data.
It performs well in both 2-D and 3-D electromagnetic scattering scenarios.
Numerical experiments validate the effectiveness of the proposed method.
Abstract
A deep learning scheme is proposed to solve the electromagnetic (EM) scattering problems where the profile of the dielectric scatterer of interest is incomplete. As a compensation, a limited amount of scattering data is provided, which is in principle containing sufficient information associated with the missing part of the profile. The existing solvers can hardly realize the compensation if the known part of the profile and the scattering data are combined straightforwardly. On one hand, the well-developed forward solvers have no mechanism to accept the scattering data, which can recover the unknown part of the profile if properly used. On the other hand, the existing solvers for inverse problems cannot retrieve the complete profile with an acceptable accuracy from the limited amount of scattering data, even when the available part of the profile can be fed into the solvers. This work…
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Taxonomy
TopicsElectromagnetic Scattering and Analysis
