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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

A physically informed deep-learning approach for locating sources in a waveguide.

Adar Kahana¹, Symeon Papadimitropoulos¹, Eli Turkel¹

¹Department of Applied Mathematics, Tel Aviv University, Tel Aviv 69978, Israel.

The Journal of the Acoustical Society of America

|October 24, 2023

Summary

This summary is machine-generated.

This study introduces a new physically informed neural network method to overcome resolution limits in inverse source problems. The technique accurately locates closely spaced sources, improving imaging in fields like acoustics and geophysics.

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Area of Science:

Wave physics
Computational electromagnetics
Applied mathematics

Background:

Inverse source problems are crucial in acoustics, geophysics, and non-destructive testing.
Conventional imaging techniques face resolution limits, hindering the ability to distinguish sources closer than the wavelength.

Purpose of the Study:

To develop a super-resolution imaging method for inverse source problems.
To address the challenge of distinguishing closely spaced sources using wave propagation physics.

Main Methods:

Implementation of physically informed neural networks (PINNs).
Development of a novel loss term incorporating wave propagation physics for super-resolution.
Application to imaging an unknown number of point sources in a 2D rectangular waveguide.

Main Results:

The PINN method successfully approximated source locations with high accuracy.
The approach demonstrated super-resolution capabilities, distinguishing sources closer than the wavelength.
Effective imaging was achieved using wavefield recordings along a vertical cross-section.

Conclusions:

Physically informed neural networks offer a powerful solution for overcoming resolution limits in inverse source problems.
The novel loss term enhances the network's ability to achieve super-resolution.
This method shows significant potential for advanced imaging applications.