Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more. Traditional imaging methods suffer from the resolution limit, preventing distinction of sources separated by less than the emitted wavelength. In this work we propose a method based on physically-informed neural-networks for solving the source refocusing problem, constructing a novel loss term which promotes super-resolving capabilities of the network and is based on the physics of wave propagation. We demonstrate the approach in the setup of imaging an a-priori unknown number of point sources in a two-dimensional rectangular waveguide from measurements of wavefield recordings along a vertical cross-section. The results show the ability of the method to approximate the locations of sources with high accuracy, even when placed close to each other.
@article{arxiv.2208.04938,
title = {A physically-informed Deep-Learning approach for locating sources in a waveguide},
author = {Adar Kahana and Symeon Papadimitropoulos and Eli Turkel and Dmitry Batenkov},
journal= {arXiv preprint arXiv:2208.04938},
year = {2022}
}