Ledger, P and Lionheart, B (2022) Minimal Object Characterisations using Harmonic Generalised Polarizability Tensors and Symmetry Groups. SIAM Journal on Applied Mathematics, 82 (6). pp. 2057-2079. ISSN 1095-712X

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We introduce a new type of object characterisation, which is capable of accurately describing small iso�lated inclusions for potential field inverse problems such as in electrostatics, magnetostatics and related
low frequency Maxwell problems. Relevant applications include characterising ferrous unexploded ordnance
(UXO) from magnetostatic field measurements in magnetometry, describing small conducting inclusions
for medical imaging using electrical impedance tomography (EIT), performing geological ground surveys
using electrical resistivity imaging (ERT), characterising objects by electrosensing fish to navigate and
identify food as well as describing the effective properties of dilute composites. Our object characterisa�tion builds on the generalised polarizability tensor (GPT) object characterisation concept and provides an
alternative to the compacted GPT (CGPT). We call the new characterisations harmonic GPTs (HGPTs)
as their coefficients correspond to products of harmonic polynomials. Then, we show that the number of
independent coefficients of HGPTs needed to characterise objects can be significantly reduced by consider�ing the symmetry group of the object and propose a systematic approach for determining the subspace of
symmetric harmonic polynomials that is fixed by the group and its dimension. This enable us to determine
the independent HGPT coefficients for different symmetry groups.

Item Type: Article
Additional Information: The final version of this article and all relevant information related to it, including copyrights, can be found on the publisher website.
Uncontrolled Keywords: Inverse problems, generalised polarizability tensor, object characterisation, symmetry groups, magnetometry, electrical impedance tomography
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 09 Aug 2022 10:08
Last Modified: 13 Apr 2023 08:46
URI: https://eprints.keele.ac.uk/id/eprint/11242

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