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Machine Learning Aided Modeling of Granular Materials: A Review

MENGQI WANG, Krishna Kumar, Yuntian Feng Orcid Logo, Tongming Qu, Min Wang

Archives of Computational Methods in Engineering

Swansea University Authors: MENGQI WANG, Yuntian Feng Orcid Logo

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DOI (Published version): 10.1007/s11831-024-10199-z

Abstract

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 detail...

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Published in: Archives of Computational Methods in Engineering
ISSN: 1134-3060 1886-1784
Published: Springer Science and Business Media LLC 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68074
first_indexed 2024-10-28T09:55:06Z
last_indexed 2025-01-09T20:32:33Z
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spelling 2024-12-12T15:46:43.4302075 v2 68074 2024-10-28 Machine Learning Aided Modeling of Granular Materials: A Review c8b7876c8a7f8a18edfa20b3aa0cb111 MENGQI WANG MENGQI WANG true false d66794f9c1357969a5badf654f960275 0000-0002-6396-8698 Yuntian Feng Yuntian Feng true false 2024-10-28 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. Journal Article Archives of Computational Methods in Engineering 0 Springer Science and Business Media LLC 1134-3060 1886-1784 29 10 2024 2024-10-29 10.1007/s11831-024-10199-z COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-12-12T15:46:43.4302075 2024-10-28T09:47:51.4139242 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering MENGQI WANG 1 Krishna Kumar 2 Yuntian Feng 0000-0002-6396-8698 3 Tongming Qu 4 Min Wang 5
title Machine Learning Aided Modeling of Granular Materials: A Review
spellingShingle Machine Learning Aided Modeling of Granular Materials: A Review
MENGQI WANG
Yuntian Feng
title_short Machine Learning Aided Modeling of Granular Materials: A Review
title_full Machine Learning Aided Modeling of Granular Materials: A Review
title_fullStr Machine Learning Aided Modeling of Granular Materials: A Review
title_full_unstemmed Machine Learning Aided Modeling of Granular Materials: A Review
title_sort Machine Learning Aided Modeling of Granular Materials: A Review
author_id_str_mv c8b7876c8a7f8a18edfa20b3aa0cb111
d66794f9c1357969a5badf654f960275
author_id_fullname_str_mv c8b7876c8a7f8a18edfa20b3aa0cb111_***_MENGQI WANG
d66794f9c1357969a5badf654f960275_***_Yuntian Feng
author MENGQI WANG
Yuntian Feng
author2 MENGQI WANG
Krishna Kumar
Yuntian Feng
Tongming Qu
Min Wang
format Journal article
container_title Archives of Computational Methods in Engineering
container_volume 0
publishDate 2024
institution Swansea University
issn 1134-3060
1886-1784
doi_str_mv 10.1007/s11831-024-10199-z
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
description 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.
published_date 2024-10-29T08:35:43Z
_version_ 1821393859534389248
score 11.080252

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