A full-field image conversion method for the inverse conductivity problem with internal measurements
Author(s) -
Cédric Bellis,
Hervé Moulinec
Publication year - 2016
Publication title -
proceedings of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 1471-2946
pISSN - 1364-5021
DOI - 10.1098/rspa.2015.0488
Subject(s) - fourier transform , scalar (mathematics) , inverse problem , conductivity , convergence (economics) , mathematical analysis , scalar field , fourier series , inverse , field (mathematics) , stability (learning theory) , mathematics , algorithm , computer science , physics , geometry , quantum mechanics , machine learning , pure mathematics , economics , mathematical physics , economic growth
International audienceThis article investigates a Fourier-based algorithm for computing heterogeneous material parameter distributions from internal measurements of physical fields. Within the framework of the periodic scalar conductivity model, a pair of dual Lippmann– Schwinger integral equations is derived for the sought constitutive parameters based on full intensity or current density field measurements. A numerical method based on the fast Fourier transform and fixed-point iterations is proposed. Convergence, stability and approximation quality of the method are analysed. For materials with small contrast, a first-order Born-like approximation is also obtained. Overall, the proposed reconstruction approach enables a direct conversion of full-field measurement images, possibly noisy, into maps of material conductivity. A set of numerical results is presented to illustrate the performance of the method
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