[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290572-105":59,"doc-detail-290572-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","machine-learning-based-approach-to-predict-ductile-damage-model-parameters-for-polycrystalline-metals","Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals","","Ductile material damage models often require parameterization that depends on loading conditions, making inverse identification computationally expensive because each condition needs a separate high-fidelity simulation campaign. Traditional inverse modeling can involve hundreds to thousands of runs and demands substantial expert effort to set up the inverse workflow. This work applies an offline-trained machine learning inverse model to rapidly estimate TEPLA ductile damage parameters from loading conditions and material type. Accuracy is shown on a synthetic dataset and validated using experimental data.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-based-approach-to-predict-ductile-damage-model-parameters-for-polycrystalline-metals/290572/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-based-approach-to-predict-ductile-damage-model-parameters-for-polycrystalline-metals/290572.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is parameterizing ductile damage models computationally expensive?","Question",{"text":112,"@type":113},"Because inverse problem solving is needed for each loading condition, typically requiring hundreds to thousands of high-fidelity computer simulations and expert time to configure the inverse model.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What does the machine learning approach do in this work?",{"text":117,"@type":113},"It uses an offline-trained machine learning inverse model to rapidly generate the TEPLA ductile damage model parameters from specific loading conditions and material type.",{"name":119,"@type":110,"acceptedAnswer":120},"How are the results evaluated?",{"text":121,"@type":113},"The method demonstrates good accuracy on a synthetic test dataset and is validated against experimental data.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290572,1789642108,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","arXiv :2301 .07790v2 [ cond-mat .mtrl-sci ] 26 May 2023  \nLA-UR-23-20468  \nMachine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals  \nD. N. Blaschke 1 , T. Nguyen2 , M. Nitol3 , D. O’Malley4 , S. Fensin3  \n1 XCP-5, Los Alamos National Laboratory  \n2 T-3, Los Alamos National Laboratory  \n3 MPA-CINT, Los Alamos National Laboratory  \n4 EES-16, Los Alamos National Laboratory  \nMay 25, 2023,  \nEmail: [dblaschke@lanl.gov](dblaschke@lanl.gov), [thao.nguyen.me@lanl.gov](thao.nguyen.me@lanl.gov), [mash@lanl.gov](mash@lanl.gov), [omalled@lanl.gov](omalled@lanl.gov),  \n[saryuj@lanl.gov](saryuj@lanl.gov)  \nAbstract  \nDamage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. This work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.  \n1 Introduction  \nDamage and failure in ductile materials is a complex function of microstructure and loading conditions. Previous work has shown that microstructural features such as grain size, texture, and dislocation density can all affect spall strength by altering the number of void nucleation sites. Additionally, for a given microstructure altering the peak velocity, tensile strain rate, and pulse duration (loading conditions) can all modify the amount of damage in a ductile material. Currently, ductile damage in materials is represented using various models such as TEPLA, TONKS, MNAG etc. [1–6] . However, these models need to be parameterized using experimental velocity-time and porosity-position data. As the loading conditions are altered, the models need to be re-parameterized. This not only makes the parameterization of models time consuming but also highlights the non-predictive nature of these models. To overcome some of these challenges, we propose to use a machine learning model that can predict the parameters of a TEPLA model rapidly given specific loading conditions and material type.  \nMachine learning is becoming an increasingly important part of material science [7] . Examples of applications include: enabling rapid prototyping of new materials on a computer rather than in a laboratory [8], advances in batteries [9], identification of perovskite compositions for solar cells [10], among many others. In data poor settings, scientists often must identify key features that can make learning easier for the model. However, in scenarios with relatively large datasets, deep learning  \nalgorithms can do this feature identification automatically [11] . Some work more closely related to this manuscript investigated fracture dynamics in materials [12] . Machine learning has been used to accelerate high-strain models of brittle fracture at the continuum scale by training a machine learning surrogate of a mesoscale model, which is used to inform a continuum scale model [13] .  \nInverse modeling [14] and the closely related field of inverse design [15] are another fertile ","cbCaisnYm31H8pEs","https://ap.wps.com/l/cbCaisnYm31H8pEs","pdf",1539649,13,"English","# Abstract\n# 1 Introduction\n## Damage and failure mechanisms in ductile materials\n## Machine learning in materials science and inverse modeling\n## Proposed workflow and experimental context","[{\"question\":\"Why is parameterizing ductile damage models computationally expensive?\",\"answer\":\"Because inverse problem solving is needed for each loading condition, typically requiring hundreds to thousands of high-fidelity computer simulations and expert time to configure the inverse model.\"},{\"question\":\"What does the machine learning approach do in this work?\",\"answer\":\"It uses an offline-trained machine learning inverse model to rapidly generate the TEPLA ductile damage model parameters from specific loading conditions and material type.\"},{\"question\":\"How are the results evaluated?\",\"answer\":\"The method demonstrates good accuracy on a synthetic test dataset and is validated against experimental data.\"}]","Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals | PDF",33]