An autoencoder solution for the electromagnetic inverse source problem

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.