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

TL;DR

This paper introduces a physics-informed neural network method to identify rheological parameters and reconstruct pressure fields in granular materials from simple experiments, demonstrating promising results with synthetic data.

Contribution

The work presents a novel PINN-based approach for rheological parameter estimation in granular media using minimal experimental data, enabling pressure field reconstruction.

Findings

Successfully infers rheological parameters from synthetic data.

Reconstructs pressure fields that are hard to measure experimentally.

Shows potential for application to real experimental data.

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.