Machine Learning Aided Modeling of Granular Materials: A Review

TL;DR

This review comprehensively discusses recent advances in applying machine learning techniques to model granular materials, covering microscopic interactions, neural network models, and macroscopic simulations for engineering applications.

Contribution

It provides a detailed overview of how machine learning has been integrated into granular material modeling, highlighting recent developments and comparing different neural network approaches.

Findings

Machine learning enhances microscopic particle interaction models.

Neural networks effectively learn constitutive behavior of granular materials.

Combining neural networks with numerical methods improves macroscopic simulations.

Abstract

Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.