[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118680-en":3,"doc-seo-118680-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118680,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning Aided Modeling of Granular Materials - A Review","Machine learning is increasingly applied to granular materials, spanning from grain-level particle–particle interactions to macroscopic granular-flow simulations. This review systematizes recent advances in ML-aided studies, beginning with ML use for microscopic interaction and contact models. It then surveys and compares neural networks for learning constitutive behavior, and concludes with ML-assisted macroscopic simulations for engineering and boundary-value problems combining neural networks with numerical methods.","arXiv :2410 . 14767v1 [physics .geo-ph] 18 Oct 2024  \nMachine Learning Aided Modeling of Granular Materials: A  \nReview  \nMengqi Wanga , Krishna Kumarb , Y. T. Fenga,∗∗, Tongming Quc , Min Wangd,∗∗  \naZienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Swansea University,  \nSwansea, Wales, SA1 8EP, UK  \nb Department of Civil, Architecture and Environmental Engineering, University of Texas at Austin, Austin,  \nTexas, 78701, USA.  \nc Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology,  \nClearwater Bay, Kowloon, Hong Kong SAR, China.  \nd Fluid Dynamics and Solid Mechanics Group, Theoretical Division, Los Alamos National Laboratory, Los  \nAlamos, New Mexico 87545, USA  \nAbstract  \nArtificial intelligence (AI) has become a buzz word since Google’s AlphaGo beat a worldchampion 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 learningaided modelling of granular materials via this comprehensive review work.  \nKeywords: Granular materials, Path-dependent stress-strain response, Machine learning, Discrete element modelling, Multiscale modelling.  \nHighlights  \n• Application of existing ML algorithms to the particle-particle interaction models  \n∗  \n∗∗ Corresponding author  \n[Email addresses:](Email addresses: y.feng@swansea.ac.uk)[ y.feng@swansea.ac.uk](Email addresses: y.feng@swansea.ac.uk) (Y. T. Feng), [minw@lanl.gov](minw@lanl.gov) (Min Wang)  \n• Constitutive study using different ML algorithms and their comparisons  \n• ML-aided macroscopic (deformation or flow) simulations of granular materials  \n1. Introduction  \nThe granular material, as a macroscopic continuum, showcases complicated features, involving anisotropy (Petalas et al., 2020 ; Ueda and Iai, 2019), strain localization (Ueda and Iai, 2019 ; Voyiadjis et al., 2005), non-coaxiality (Tian and Yao, 2017), and path-and-states dependence (Das and Das, 2019 ; Alipour and Lashkari, 2018 ; Hu et al., 2018), under external loading due to their micro-discrete nature. Traditional numerical techniques, such asthe finite element method (FEM), finite differential method (FDM), material point method (MPM), discrete element method (DEM), and smoothed particle hydrodynamics (SPH), have been widely employed to investigate the micro/discrete and macro/continuous duality (Wood, 2017) of granular media. Wherein the FEM, SPH, and MPM solve the mechanical responses of granular materials from the macroscopic scale, while the DEM focuses on the mechanical behaviour of granular media at the microscale.  \nIn the mesh-based numerical method, such as FEM (Zienkiewicz et al., 2005), the research domain is discretized into finite element meshes that incorporate Gaussian points or grids, and the local mechanical response of the material is characterized by continuumtheory-based phenomenological models embedded in each Gaussian point. The classic constitutive models used in these methods include linear elasticity (e.g. Hooke’s law), nonlinear elasticity (e.g. Duncan-Chang (Duncan and Chang, 1970)), the elastic-perfectly-plastic model (e.g. Mohr-Coulomb model (Ti et al., 2009), Drucker-","cbCaigmipoW0tPTJ","https://ap.wps.com/l/cbCaigmipoW0tPTJ","pdf",14179895,1,74,"English","en",105,"# Abstract\n## Introduction","[{\"question\":\"What scope does the review cover in ML-aided granular material modeling?\",\"answer\":\"It covers advances from microscopic particle–particle interaction and contact models to macroscopic simulations of granular flow.\"},{\"question\":\"How is machine learning used for granular constitutive behavior in the review?\",\"answer\":\"The review surveys different neural networks aimed at learning constitutive behavior and compares their approaches.\"},{\"question\":\"What simulation level is emphasized for engineering and boundary-value problems?\",\"answer\":\"It discusses macroscopic (deformation or flow) simulations that combine neural networks with numerical methods.\"}]","Machine Learning Aided Modeling of Granular Materials - A Review | PDF",1785684861,186,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-aided-modeling-of-granular-materials-a-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-aided-modeling-of-granular-materials-a-review/118680/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What scope does the review cover in ML-aided granular material modeling?","Question",{"text":75,"@type":76},"It covers advances from microscopic particle–particle interaction and contact models to macroscopic simulations of granular flow.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is machine learning used for granular constitutive behavior in the review?",{"text":80,"@type":76},"The review surveys different neural networks aimed at learning constitutive behavior and compares their approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What simulation level is emphasized for engineering and boundary-value problems?",{"text":84,"@type":76},"It discusses macroscopic (deformation or flow) simulations that combine neural networks with numerical methods.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]