Jacobian of solutions to the conductivity equation in limited view
Salo, M., & Schlüter, H. (2022). Jacobian of solutions to the conductivity equation in limited view. Inverse Problems, 39(2), Article 025001. https://doi.org/10.1088/1361-6420/aca904
Julkaistu sarjassa
Inverse ProblemsPäivämäärä
2022Oppiaine
MatematiikkaInversio-ongelmien huippuyksikköMathematicsCentre of Excellence in Inverse ProblemsTekijänoikeudet
© 2022 IOP Publishing Ltd.
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.
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IOP PublishingISSN Hae Julkaisufoorumista
0266-5611Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/164455865
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Rahoittaja(t)
Euroopan komissio; Suomen AkatemiaRahoitusohjelmat(t)
Huippuyksikkörahoitus, SA
The content of the publication reflects only the author’s view. The funder is not responsible for any use that may be made of the information it contains.
Lisätietoja rahoituksesta
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).Lisenssi
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