A Deep Learning Scheme of Electromagnetic Scattering From Scatterers With Incomplete Profiles

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 for the incompletion of the profile, a limited amount of scattering/prescribed data are provided, which in principle contain sufficient information associated with the missing part of the profile. The existing solvers can hardly solve this type of problems. On one hand, the well-developed forward solvers have no mechanism to accept the scattering data to recover the unknown part of the profile. 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. To overcome the difficulty, the EM forward scattering from an incompletely known dielectric scatterer is derived in this work. A deep learning scheme is then developed where the forward and inverse scattering problems can be solved simultaneously. Numerical experiments are conducted to demonstrate the performance of the proposed DL-based scheme in terms of two-dimensional (2-D) EM scattering problems.