[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117265-en":3,"doc-seo-117265-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117265,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine Learning Aided Modeling of Granular Materials: A Review","Machine learning has gained sustained research interest in granular materials over the past five years, bridging AI approaches to grain-level interactions and engineering-scale granular flow simulations. This review traces progress from particle–particle interactions and contact models, to neural-network methods that learn granular constitutive behavior, comparing alternative network formulations. It then discusses macroscopic simulations for practical boundary-value and engineering problems achieved by combining neural networks with numerical techniques, aiming to clarify the development of ML-aided granular modeling.","Archives of Computational Methods in Engineering [https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1831-024-10199-z  \nMachine Learning Aided Modeling of Granular Materials: A Review  \nMengqi Wang1 · Krishna Kumar2 · Y. T. Feng1 · Tongming Qu3 · Min Wang4  \nReceived: 19 June 2024 / Accepted: 17 October 2024 © The Author(s) 2024  \nAbstract  \nArtificial 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.  \n1 Introduction  \nThe granular material, as a macroscopic continuum, showcases complicated features, involving anisotropy [125, 155], strain localization [155 , 158], non-coaxiality [154], and path-and-states dependence [2, 41, 79], under external loading due to their micro-discrete nature. Traditional numerical techniques, such as the 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 [173]  \n* Y. T. Feng [y.feng@swansea.ac.uk](y.feng@swansea.ac.uk)  \n* Min Wang [minw@lanl.gov](minw@lanl.gov)  \n1 Zienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Swansea University, Swansea, Wales SA1 8EP, UK  \n2 Department of Civil, Architecture and Environmental Engineering, University of Texas at Austin, Austin, Texas 78701, USA  \n3 Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong SAR, China  \n4 Fluid Dynamics and Solid Mechanics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA  \nof 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 [196], 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 continuum-theory-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 [43]), the elasticperfectly-plastic model (e.g. Mohr-Coulomb model [153], Drucker-Prager model, and hardening soil (HS) model [23]), and the critical-state-based model (e.g. modified cam-clay MCC [137] UH [179, 180], and hypoplastic model [118, 177]), etc.  \nDifferent from the mesh-based method, the MPM, a hybrid Eulerian-Lagrangian meshless approach [71, 186], governs the macroscopic deformation of the target body via a set of discretized material points which carry local physical features (e.g. mass, density, and velocity) of the material. In MPM, the information stored on each material point is first projected to the node of the Euleri","cbCaib6SWP76v14u","https://ap.wps.com/l/cbCaib6SWP76v14u","pdf",13656842,1,38,"English","en",105,"# Introduction\n## Machine learning-aided granular modeling: scope and goals\n## Traditional numerical methods and micro/macro duality\n## Mesh-based methods (FEM, SPH, MPM)\n## Discrete element method (DEM) and contact modeling\n## Limitations of conventional constitutive and numerical approaches","[{\"question\":\"What scope does the review cover in machine learning-aided granular modeling?\",\"answer\":\"It covers developments from grain-level particle–particle interactions and contact models to neural-network-based constitutive behavior learning and then macroscopic granular flow simulations for engineering boundary-value problems.\"},{\"question\":\"How does the review distinguish common numerical methods for granular materials?\",\"answer\":\"It contrasts mesh-based continuum approaches (e.g., FEM, SPH, MPM) that solve macroscopic responses with DEM that explicitly models particle interactions and contact effects at the microscale.\"},{\"question\":\"Why do traditional constitutive and numerical methods have limitations for granular materials?\",\"answer\":\"The review highlights issues such as smearing discrete features in mesh-based models, which restricts simulation of catastrophic instabilities, and the increasing complexity and calibration burden of sophisticated constitutive models that may rely on assumptions without clear physical meaning.\"}]",1785674871,96,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-aided-modeling-of-granular-materials-a-review","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-aided-modeling-of-granular-materials-a-review/117265/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What scope does the review cover in machine learning-aided granular modeling?","Question",{"text":74,"@type":75},"It covers developments from grain-level particle–particle interactions and contact models to neural-network-based constitutive behavior learning and then macroscopic granular flow simulations for engineering boundary-value problems.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the review distinguish common numerical methods for granular materials?",{"text":79,"@type":75},"It contrasts mesh-based continuum approaches (e.g., FEM, SPH, MPM) that solve macroscopic responses with DEM that explicitly models particle interactions and contact effects at the microscale.",{"name":81,"@type":72,"acceptedAnswer":82},"Why do traditional constitutive and numerical methods have limitations for granular materials?",{"text":83,"@type":75},"The review highlights issues such as smearing discrete features in mesh-based models, which restricts simulation of catastrophic instabilities, and the increasing complexity and calibration burden of sophisticated constitutive models that may rely on assumptions without clear physical meaning.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & 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