In this contribution, authors propose a new method for electromagnetic inverse scattering problem, which gains advantages by deep learning as well as the paradigms of 'virtual experiments'.
Electromagnetic Inverse Scattering via Deep Learning Enhanced by Virtual Experiments / Bevacqua, M. T.; Ieracitano, C.; Mammone, N.; Morabito, F. C.; Isernia, T.; Di Donato, L.. - (2023), pp. 1231-1236. ( 2023 Photonics and Electromagnetics Research Symposium, PIERS 2023 cze 2023) [10.1109/PIERS59004.2023.10221556].
Electromagnetic Inverse Scattering via Deep Learning Enhanced by Virtual Experiments
Ieracitano C.;
2023
Abstract
In this contribution, authors propose a new method for electromagnetic inverse scattering problem, which gains advantages by deep learning as well as the paradigms of 'virtual experiments'.File in questo prodotto:
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