Three-dimensional sound field reconstruction from optical projections using physics-informed neural networks.

Abstract

The implicit representation by physics-informed neural networks (PINNs) serves as an effective solution for a key challenge faced by optical sound measurements. Since optical sound measurements observe line integral of the sound pressure along the optical path, reconstruction is necessary to determine the sound pressure at each point in the three-dimensional field. In this paper, we expand the PINNs-based reconstruction method into three-dimensional reconstruction and demonstrate its effectiveness for optically measured sound fields. Furthermore, we propose a reconstruction approach which can estimate solutions well outside the bounds of the data used for training.