A physically-informed Deep-Learning approach for locating sources in a waveguide

• URL: http://arxiv.org/abs/2208.04938v1
• Date: Sun, 7 Aug 2022 19:54:10 GMT
• Title: A physically-informed Deep-Learning approach for locating sources in a waveguide
• Authors: Adar Kahana, Symeon Papadimitropoulos, Eli Turkel, Dmitry Batenkov
• Abstract summary: 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. We propose a method based on physically-informed neural-networks for solving the source refocusing problem.
• Score: 0.688204255655161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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