A mesh-refinement method based on artificial neural networks for electrical impedance tomography

Sebastien Martin, T.m. Choi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Inverse boundary problem are usually solved with a finite element model, with either limited accuracy or huge computation resources. Using a fine mesh results in a computationally demanding task, but when the region of interest is known, when one can easily locally refine the model, aiming for greater local accuracy. In this paper, a novel approach uses artificial neural network to estimate the location of the region of interest and refine this region before solving the inverse problem. The idea is illustrated by solving the electrical impedance tomography inverse problem. Result shows that the proposed method increases the accuracy without significantly affecting the computation resources necessary to solve the inverse problem.

Original languageEnglish
Title of host publicationIEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509010325
DOIs
StatePublished - 12 Jan 2017
Event17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016 - Miami, United States
Duration: 13 Nov 201616 Nov 2016

Publication series

NameIEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation

Conference

Conference17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016
CountryUnited States
CityMiami
Period13/11/1616/11/16

Keywords

  • Adaptive Mesh Refinement
  • Artificial Neural Network
  • Electrical Impedance Tomography
  • Finite Element Method

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