A Qualitative Deep Learning Method for Inverse Scattering Problems

He Yang, Jun Liu

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a novel deep convolutional neural network (CNN) based qualitative learning method for solving the inverse scattering problem, which is notoriously difficult due to its highly nonlinearity and ill-posedness. The trained deep CNN accurately approximates the nonlinear mapping from the noisy far-field pattern (from measurements) to a disk that fits the location and size of the unknown scatterer. The used training data is derived from the simulated noisy-free far-field patterns of a large number of disks with different randomly generated centers and radii within the domain of interest. The reconstructed fitting disk is also very useful as a good initial guess for other established nonlinear optimization algorithms. Numerical results are presented to illustrate the promising reconstruction accuracy and efficiency of our proposed qualitative deep learning method.
Original languageEnglish (US)
Pages (from-to)153-160
Number of pages8
JournalApplied Computational Electromagnetics Society Journal
Volume35
Issue number2
StatePublished - 2020

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Keywords

  • Convolutional Neural Networks
  • Deep learning
  • inverse acoustic scattering
  • qualitative method

ASJC Scopus subject areas

  • Applied Mathematics
  • Physics and Astronomy(all)
  • Artificial Intelligence

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