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

arXiv: https://arxiv.org/abs/2208.04938 · HTML: https://mlsanity.com/paper/2208.04938/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 7 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Source localization in an ocean waveguide using supervised machine learning](https://mlsanity.com/paper/1701.08431/) (2017)
- [Deep Residual Network for Sound Source Localization in the Time Domain](https://mlsanity.com/paper/1808.06429/) (2018)
- [Deep-learning source localization using multi-frequency magnitude-only data](https://mlsanity.com/paper/1903.12319/) (2019)
- [Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime](https://mlsanity.com/paper/2106.01143/) (2021)

## Abstract (arXiv)

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.