Reconstructing the pressure field around swimming fish using a physics-informed neural network.

Abstract

Fish detect predators, flow conditions, environments, and each other through pressure signals. Lateral line ablation is often performed to understand the role of pressure sensing. In this study, the authors propose a non-invasive method for reconstructing the instantaneous pressure field sensed by a fish's lateral line system from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the pressure field near and on the fish's body that satisfy both the Navier-Stokes equations and the constraints put forward by the PIV measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to experimental data of a turning zebrafish, Danio rerio. The results demonstrate that this method is relatively insensitive to the spatio-temporal resolution of the PIV measurements and accurately reconstructs the pressure on the fish's body.