Reconstructing the pressure field around swimming fish using a physics-informed neural network.
J Exp Biol
; 226(8)2023 04 15.
Article
en En
| MEDLINE
| ID: mdl-37066991
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 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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Natación
/
Pez Cebra
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
J Exp Biol
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos