Optical interferometry based acoustic field characterization using physics informed neural networks

The acousto-optic effect offers a non-contact way of measuring and visualizing acoustic fields in transparent fluids. Acoustic density fluctuations induce changes in the media's refractive index that can be measured using interferometry. While sensing methods based on acousto-optics have gathered increasing attention, many of such methods are only sensitive to projections of the acoustic field along the optical path, limiting the fields that can be characterized to one or two dimensions, or requiring complex experimental setups. Recently, reconstruction methods that abide by the physics of acoustic wave propagation showed unparalleled performance compared with classical reconstruction methods, reducing the sampling requirements and enabling to characterize three-dimensional acoustic fields. Nonetheless, such reconstruction methods rely on discrete approximations which might be poor at describing spatially complex fields. In this work we use physics-informed neural networks (PINNs) to estimate the acoustic pressure over space from interferometer measurements. The network is flexible enough to represent complex acoustic fields, while its output is constrained to fulfill the wave equation. Once trained on the data available for a given experiment, the PINN can estimate the acoustic pressure at arbitrary positions. The approach is tested in an experimental study, showing an improved performance compared with state-of-the-art reconstruction methods.