24 citations · 25 across the 4 of their papers we have counts for
4 papers
Rheological Parameter Identification in Granular Materials Using Physics-Informed Neural Networks
Barbara Baldoni, Mickaël Delcey, Yoann Cheny +3
Physics-Informed Neural Networks (PINNs) have recently emerged as a promising tool for fluid dynamics, particularly for flow reconstruction and parameter identification. In the con…
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…
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-informed neural networks for gravity currents reconstruction from limited data
Mickaël Delcey, Yoann Cheny, Sébastien Kiesgen de Richter
The present work investigates the use of physics-informed neural networks (PINNs) for the 3D reconstruction of unsteady gravity currents from limited data. In the PINN context, the…