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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sami Barmada & Paolo Di Barba & Nunzia Fontana & Maria Evelina Mognaschi & Mauro Tucci, 2024. "A Source Identification Problem in Magnetics Solved by Means of Deep Learning Methods," Mathematics, MDPI, vol. 12(6), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:859-:d:1357472
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    References listed on IDEAS

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    1. Zichao Jin & Yue Cao & Shuwang Li & Wenjun Ying & Mahesh Krishnamurthy, 2023. "Analytical Approach for Sharp Corner Reconstruction in the Kernel Free Boundary Integral Method during Magnetostatic Analysis for Inductor Design," Energies, MDPI, vol. 16(14), pages 1-16, July.
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