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 the present study, we propose a non-invasive method for reconstructing the instantaneous pressure field sensed by a fish's lateral line system from two-dimensional 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 satisfies 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 zebrafis

SUBMITTER: Calicchia MA 

PROVIDER: S-EPMC10163353 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature