arXiv subjects

Yoann Cheny

Publications and source records attributed to Yoann Cheny.

4 recordsLinked to original sources

arXiv2025

Rheological Parameter Identification in Granular Materials Using Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have recently emerged as a promising tool for fluid dynamics, particularly for flow reconstruction and parameter identification. In the context of granular media, accurately estimating rheological parameters remains a major challenge, as it typically requires complex and costly experimental setups. In this work, we propose a PINN-based approach to identify key rheological parameters of granular materials using a simple experiment: the granular column collapse. A proof of concept is presented using synthetic data, where the PINN is trained to infer the flow fields while simultaneously recovering the rheological parameters. Beyond parameter identification, the method also enables reconstruction of the pressure field, which is difficult to access experimentally. The results highlight the potential of PINNs for data-driven rheometry of granular materials and open perspectives for future applications with real experimental data.

cond-mat.dis-nn↗

arXiv2024

Identification of Settling Velocity with Physics Informed Neural Networks For Sediment Laden Flows

Physics-Informed Neural Networks (PINNs) have shown great potential in the context of fluid dynamics simulations, particularly in reconstructing flow fields and identifying key parameters. In this study, we explore the application of PINNs to recover the dimensionless settling velocity for sedimentation flow. The flow involves sediment-laden fresh water overlying salt water, which is described by Navier-Stokes equations coupled with sediment concentration and salinity transport equations. Two cases are investigated: one where the training data contains the salinity and sediment concentration fields, and another where it contains the velocity field. For both cases, we investigate several flow regimes and show that the model is capable of inferring the unknown parameter and reconstructing the hydrodynamic field of the flow. The quality of the model inference is assessed by comparing it with numerical simulations from a high-fidelity semi-Lagrangian solver. We demonstrate the model's robustness to noise by training it with data corrupted by noise of varying magnitudes, highlighting the potential of PINNs for real-world applications.

physics.flu-dyn↗

arXiv2023

Quantitative Assessment of PINN Inference on Experimental Data for Gravity Currents Flows

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 induced by lock-exchange. In a PINN model, physical laws are embedded in the loss function of a neural network, such that the model fits the training data but is also constrained to reduce the residuals of the governing equations. PINNs are able to solve ill-posed inverse problems training on sparse and noisy data, and therefore can be applied to real engineering applications. The noise robustness of PINNs and the model parameters are investigated in a 2 dimensions toy case on a lock-exchange configuration, employing synthetic data. Then we train a PINN with experimental LAT measurements and quantitatively compare the velocity fields inferred to Particle Image Velocimetry (PIV) measurements performed simultaneously on the same experiment.The results state that accurate and useful quantities can be derived from a PINN model trained on real experimental data which is encouraging for a better description of gravity currents.

physics.flu-dyn↗

arXiv2022

Physics-informed neural networks for gravity currents reconstruction from limited data

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 flow fields are reconstructed by training a neural network whose objective function penalizes the mismatch between the network predictions and the observed data and embeds the underlying equations using automatic differentiation. This study relies on a high-fidelity numerical experiment of the canonical lock-exchange configuration. This allows us to benchmark quantitatively the PINNs reconstruction capabilities on several training databases that mimic state-of-the-art experimental measurement techniques for density and velocity. Notably, spatially averaged density measurements by light attenuation technique (LAT) are employed for the training procedure. An optimal experimental setup for flow reconstruction by PINNs is proposed according to two criteria : the implementation complexity and the accuracy of the inferred fields.

physics.flu-dyn↗