This study addresses a 2D scalar electromagnetic inverse source problem by using a deep neural network-based artificial intelligence technique. Specifically, the Learned Singular Value Decomposition (L-SVD) approach based on hybrid autoencoding is adopted. The main goal is to reproduce the singular value decomposition through neural networks and compare the reconstruction performance of L-SVD and truncated SVD (TSVD) in the case of noiseless data, which represents a reference benchmark. The results demonstrate that L-SVD outperforms TSVD in terms of spatial resolution.
An autoencoder solution for the electromagnetic inverse source problem / Cinotti, E., Esposito, G., Gennarelli, G., Ludeno, G., Catapano, I., Capozzoli, A., Curcio, C., Liseno, A., Soldovieri, F.. - 12621:Volume 12621(2023), pp. 1-5. (Multimodal Sensing and Artificial Intelligence: Technologies and Applications III Munich, Germany Jun. 27-29, 2023) [10.1117/12.2675891].
An autoencoder solution for the electromagnetic inverse source problem
Cinotti E.;Capozzoli A.;Curcio C.;Liseno A.;
2023
Abstract
This study addresses a 2D scalar electromagnetic inverse source problem by using a deep neural network-based artificial intelligence technique. Specifically, the Learned Singular Value Decomposition (L-SVD) approach based on hybrid autoencoding is adopted. The main goal is to reproduce the singular value decomposition through neural networks and compare the reconstruction performance of L-SVD and truncated SVD (TSVD) in the case of noiseless data, which represents a reference benchmark. The results demonstrate that L-SVD outperforms TSVD in terms of spatial resolution.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
