Computational and Analytical Methods for Inverse Problems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 842

Special Issue Editor


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Guest Editor
Department of Engineering, Central Connecticut State University, New Britain, CT 06053, USA
Interests: inverse problems; dynamic systems; feedback controls; numerical mathematics
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Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to gather recent results on analytical and computational methods for inverse problems that appear in various physical systems. It is often the case that, given a physical system, a subsurface material property is unknown. Given a set of observations, the goal of an inverse problem is to recover the material property based on the given input/output data. Important physical applications include land/sea mine detection, breast cancer identification and many more. It is often the case that the system includes an unknown boundary condition such as surface ablation in re-entry vehicles or plasma location inside a Tokamak. Such problems are often referred to as Cauchy problems. It is well known that both Cauchy problems and inverse problems are severely ill-posed.

The aim of this Special Issue is to gather recent results on such problems. In particular, new results on computational and analytical methods for such problems are welcomed. Applications of the existing methods to various fields are also welcomed, such as imaging biological systems, recovering subsurface material properties in elastic/chemical/thermal systems, and recovering interior unknowns from boundary measurements in furnaces/Tokamaks. Theoretical results on the uniqueness, regularity and identifiability of unknown functions/boundaries are also welcomed. New results on the regularization of such problems are also welcomed.

Dr. Mohsen Tadi
Guest Editor

Manuscript Submission Information

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Keywords

  • inverse problem
  • Cauchy problem
  • regularization
  • land/sea mine detection
  • impedance tomography
  • inverse wave scattering in elastic/electromagnetic domains

Published Papers (1 paper)

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Research

8 pages, 583 KiB  
Article
Deep Neural Network-Oriented Indicator Method for Inverse Scattering Problems Using Partial Data
by Yule Lin, Xiaoyi Yan, Jiguang Sun and Juan Liu
Mathematics 2024, 12(4), 522; https://doi.org/10.3390/math12040522 - 07 Feb 2024
Viewed by 653
Abstract
We consider the inverse scattering problem to reconstruct an obstacle using partial far-field data due to one incident wave. A simple indicator function, which is negative inside the obstacle and positive outside of it, is constructed and then learned using a deep neural [...] Read more.
We consider the inverse scattering problem to reconstruct an obstacle using partial far-field data due to one incident wave. A simple indicator function, which is negative inside the obstacle and positive outside of it, is constructed and then learned using a deep neural network (DNN). The method is easy to implement and effective as demonstrated by numerical examples. Rather than developing sophisticated network structures for the classical inverse operators, we reformulate the inverse problem as a suitable operator such that standard DNNs can learn it well. The idea of the DNN-oriented indicator method can be generalized to treat other partial data inverse problems. Full article
(This article belongs to the Special Issue Computational and Analytical Methods for Inverse Problems)
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