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

TL;DR

This paper introduces a physics-informed deep learning method for high-resolution source localization in waveguides, overcoming traditional resolution limits by incorporating wave physics into the neural network training.

Contribution

The work presents a novel neural network loss function based on wave physics, enabling super-resolution in inverse source problems within waveguides.

Findings

Accurately localizes multiple sources close together

Outperforms traditional methods in resolution

Demonstrates effectiveness in 2D waveguide imaging

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.