Abstract
A deep learning-based method is developed to solve electromagnetic scattering problems, where the U-Net is combined with the conjugate gradient method (U-Net-CG). Numerical results show that the U-Net-CG outperforms the traditional CG in terms of computational speed under the comparable accuracy.
| Original language | English |
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| Title of host publication | 2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781733509619 |
| DOIs | |
| Publication status | Published - 28 Jul 2021 |
| Event | 4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021 - Chengdu, China Duration: 28 Jul 2021 → 31 Jul 2021 |
Publication series
| Name | 2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings |
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Conference
| Conference | 4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021 |
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| Country/Territory | China |
| City | Chengdu |
| Period | 28/07/21 → 31/07/21 |
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Xue, B. W., Wu, DI., Song, B. Y., Guo, R., Pan, X. M., Li, M. K., & Sheng, X. Q. (2021). U-Net Conjugate Gradient Solution of Electromagnetic Scattering from Dielectric Objects. In 2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings (2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACES-China52398.2021.9581406