Jacobian of solutions to the conductivity equation in limited view

Abstract
The aim of hybrid inverse problems such as Acousto-Electric Tomography or Current Density Imaging is the reconstruction of the electrical conductivity in a domain that can only be accessed from its exterior. In the inversion procedure, the solutions to the conductivity equation play a central role. In particular, it is important that the Jacobian of the solutions is non-vanishing. In the present paper we address a two-dimensional limited view setting, where only a part of the boundary of the domain can be controlled by a non-zero Dirichlet condition, while on the remaining boundary there is a zero Dirichlet condition. For this setting, we propose sufficient conditions on the boundary functions so that the Jacobian of the corresponding solutions is non-vanishing. In that regard we allow for discontinuous boundary functions, which requires the use of solutions in weighted Sobolev spaces. We implement the procedure of reconstructing a conductivity from power density data numerically and investigate how this limited view setting affects the Jacobian and the quality of the reconstructions.
Main Authors
Format
Articles Research article
Published
2022
Series
Subjects
Publication in research information system
Publisher
IOP Publishing
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202212285848Käytä tätä linkitykseen.
Review status
Peer reviewed
ISSN
0266-5611
DOI
https://doi.org/10.1088/1361-6420/aca904
Language
English
Published in
Inverse problems
Citation
License
CC BY-NC-ND 4.0Open Access
Funder(s)
European Commission
Academy of Finland
Funding program(s)
ERC Consolidator Grant
Huippuyksikkörahoitus, SA
ERC Consolidator Grant
Centre of Excellence, AoF
European CommissionAcademy of FinlandEuropean research council
Additional information about funding
M.S. was partly supported by the Academy of Finland (Centre of Excellence in Inverse Modelling and Imaging, grant 284715) and by the European Research Council under Horizon 2020 (ERC CoG 770924).
Copyright© 2022 IOP Publishing Ltd.

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