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Related Experiment Video

Updated: Oct 10, 2025

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy
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A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography.

Marco Olivieri1, Mirco Pezzoli1, Fabio Antonacci1

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary

This study introduces KHCNN, a novel deep learning method for nearfield acoustic holography. It accurately reconstructs vibration fields, improving accuracy by up to 10 dB compared to existing techniques.

Keywords:
Kirchhoff–Helmholtz integralconvolutional neural networkfinite element methodnearfield acoustic holography

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Nearfield acoustic holography (NAH) is crucial for analyzing vibrating structures.
  • Accurate reconstruction of pressure and velocity fields is essential for NAH.
  • Current NAH methods face limitations in accuracy and computational efficiency.

Purpose of the Study:

  • To develop a novel deep learning-based methodology for NAH.
  • To reconstruct the pressure and velocity fields on a vibrating structure's surface.
  • To improve the accuracy and efficiency of NAH techniques.

Main Methods:

  • A convolutional neural network (CNN) with an autoencoder architecture was employed.
  • The network reconstructs surface fields from sampled sound pressure on a holographic plane.
  • A physics-informed loss function incorporating the Kirchhoff-Helmholtz integral was utilized, termed KHCNN.

Main Results:

  • KHCNN demonstrated high accuracy in reconstructing pressure and velocity fields.
  • Significant improvements were observed on datasets of rectangular plates and violin shells.
  • A gain in Normalized Mean Squared Error (NMSE) for the velocity field reached up to 10 dB.

Conclusions:

  • The proposed KHCNN method offers a powerful and accurate approach to NAH.
  • Integrating physical principles into deep learning enhances reconstruction performance.
  • KHCNN represents a significant advancement over state-of-the-art NAH techniques.