Boundary Integral Neural Networks for Acoustic Radiation Prediction from Noisy Boundary Data

Accurate predictions of sound radiation are crucial for assessing sound emissions in the far field. A widely used approach is the boundary element method, which traditionally solves the Kirchhoff-Helmholtz integral equation by discretization. While the boundary integral formulation inherently satisfies the Sommerfeld radiation condition, a significant drawback of the boundary element method is its difficulty in incorporating noisy measurement data. In recent years, physics-informed machine learning approaches have demonstrated robust predictions of physical problems, even in the presence of noisy or imperfect data. To utilize these benefits while addressing acoustic predictions in unbounded domains, this study employs boundary integral neural networks for predicting acoustic radiation. These networks incorporate the residual of the boundary integral equation into the neural network’s loss function, enabling data-driven predictions of acoustic radiation from noisy boundary data. The results demonstrate that boundary integral neural networks are able to accurately predict the sound pressure field for both interior and exterior problems of a two-dimensional acoustic domain. The study also highlights that the data-driven approach outperforms the conventional boundary element method, particularly at high noise levels. Consequently, the presented method offers a promising approach for predicting sound radiation based on noisy surface vibration measurements.