Sound Field Reconstruction Using Physics-Informed Boundary Integral Networks

TL;DR

This paper introduces a boundary integral neural network for sound field reconstruction that leverages the Kirchhoff-Helmholtz equation, achieving improved accuracy over existing physics-informed methods.

Contribution

The work presents a novel boundary integral neural network approach for acoustic pressure estimation, combining boundary integral equations with neural networks for enhanced sound field reconstruction.

Findings

Outperforms existing physics-informed data-driven techniques

Accurately reconstructs acoustic pressure inside a domain

Uses boundary measurements to train the model

Abstract

Sound field reconstruction refers to the problem of estimating the acoustic pressure field over an arbitrary region of space, using only a limited set of measurements. Physics-informed neural networks have been adopted to solve the problem by incorporating in the training loss function the governing partial differential equation, either the Helmholtz or the wave equation. In this work, we introduce a boundary integral network for sound field reconstruction. Relying on the Kirchhoff-Helmholtz boundary integral equation to model the sound field in a given region of space, we employ a shallow neural network to retrieve the pressure distribution on the boundary of the considered domain, enabling to accurately retrieve the acoustic pressure inside of it. Assuming the positions of measurement microphones are known, we train the model by minimizing the mean squared error between the estimated and measured pressure at those locations. Experimental results indicate that the proposed model outperforms existing physics-informed data-driven techniques.