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

Abstract

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.