A Source Identification Problem in Magnetics Solved by Means of Deep Learning Methods

Sami Barmada, Paolo Di Barba, Nunzia Fontana, Maria Evelina Mognaschi and Mauro Tucci ()
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Sami Barmada: DESTEC Department, University of Pisa, 56122 Pisa, Italy
Paolo Di Barba: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
Nunzia Fontana: DESTEC Department, University of Pisa, 56122 Pisa, Italy
Maria Evelina Mognaschi: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
Mauro Tucci: DESTEC Department, University of Pisa, 56122 Pisa, Italy

Mathematics, 2024, vol. 12, issue 6, 1-14

Abstract: In this study, a deep learning-based approach is used to address inverse problems involving the inversion of a magnetic field and the identification of the relevant source, given the field data within a specific subdomain. Three different techniques are proposed: the first one is characterized by the use of a conditional variational autoencoder (CVAE) and a convolutional neural network (CNN); the second one employs the CVAE (its decoder, more specifically) and a fully connected deep artificial neural network; while the third one (mainly used as a comparison) uses a CNN directly operating on the available data without the use of the CVAE. These methods are applied to the magnetostatic problem outlined in the TEAM 35 benchmark problem, and a comparative analysis between them is conducted.

Keywords: deep learning; source identification problem; magnetic field; conditional variational autoencoder; automatic differentiation; image reconstruction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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