Inverse Electromagnetic Scattering for Doubly-Connected Cylinders using Convolutional Neural Networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915637880684544 |
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| author | Mindrinos, Leonidas Pallikarakis, Nikolaos Tsitsas, Nikolaos L |
| author_facet | Mindrinos, Leonidas Pallikarakis, Nikolaos Tsitsas, Nikolaos L |
| contents | In this work, we consider the inverse electromagnetic scattering problem for a magneto-dielectric cylinder covering an impedance cylinder of arbitrary shape. We solve it by introducing a divide-and-conquer framework using specially designed 1D multi-channel, circular-padding Convolutional Neural Networks. The solution of the direct problem provides us with the real and imaginary components of the far-field measurements representing the input data. We first classify the shape of the impedance cylinder and then reconstruct the unknown boundary curve and the impedance function. Through extensive numerical experiments, including noisy scenarios, we demonstrate the efficiency and robustness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inverse Electromagnetic Scattering for Doubly-Connected Cylinders using Convolutional Neural Networks Mindrinos, Leonidas Pallikarakis, Nikolaos Tsitsas, Nikolaos L Numerical Analysis 68T07, 78A46, 65N21, 35R30 In this work, we consider the inverse electromagnetic scattering problem for a magneto-dielectric cylinder covering an impedance cylinder of arbitrary shape. We solve it by introducing a divide-and-conquer framework using specially designed 1D multi-channel, circular-padding Convolutional Neural Networks. The solution of the direct problem provides us with the real and imaginary components of the far-field measurements representing the input data. We first classify the shape of the impedance cylinder and then reconstruct the unknown boundary curve and the impedance function. Through extensive numerical experiments, including noisy scenarios, we demonstrate the efficiency and robustness of our approach. |
| title | Inverse Electromagnetic Scattering for Doubly-Connected Cylinders using Convolutional Neural Networks |
| topic | Numerical Analysis 68T07, 78A46, 65N21, 35R30 |
| url | https://arxiv.org/abs/2511.20681 |