Machine Learning Aided Modeling of Granular Materials: A Review

Wang, Mengqi; Kumar, Krishna; Feng, Y. T.; Qu, Tongming; Wang, Min

doi:10.1007/s11831-024-10199-z

Title: Machine Learning Aided Modeling of Granular Materials: A Review

Journal Article · 29 October 2024 · Archives of Computational Methods in Engineering

DOI: https://doi.org/10.1007/s11831-024-10199-z · OSTI ID:2478635

Wang, Mengqi ^[1]; Kumar, Krishna ^[2]; Feng, Y. T. ^[1]; Qu, Tongming ^[3];

^[4]

Swansea University, Wales (United Kingdom)
University of Texas, Austin, TX (United States)
Hong Kong University of Science and Technology (HKUST) (Hong Kong)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Artificial intelligence (AI) has become a buzzy 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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2478635

Report Number(s):: LA-UR--24-24632

Journal Information:: Archives of Computational Methods in Engineering, Journal Name: Archives of Computational Methods in Engineering Journal Issue: 4 Vol. 32; ISSN 1134-3060

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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