Showing physics.flu-dynShow all
2 papers · 1 filter
physics.flu-dyn2025
Quantitative Assessment of PINN Inference on Experimental Data for Gravity Currents Flows
Mickaël Delcey, Yoann Cheny, Jean Schneider +2
In this paper, we apply Physics Informed Neural Networks (PINNs) to infer velocity and pressure field from Light Attenuation Technique (LAT) measurements for gravity current induce…
physics.flu-dyn2024
Identification of Settling Velocity with Physics Informed Neural Networks For Sediment Laden Flows
Mickaël Delcey, Yoann Cheny, Jean-Baptiste Keck +2
Physics-Informed Neural Networks (PINNs) have shown great potential in the context of fluid dynamics simulations, particularly in reconstructing flow fields and identifying key par…