[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124037-en":3,"doc-seo-124037-105":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},124037,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning in solid mechanics - Application to acoustic metamaterial design","Machine learning (ML) and deep learning (DL) increasingly drive advanced metamaterial design, linking material or topology optimization with efficient property prediction across large design spaces. The study proposes an ML-based optimization workflow for Multiresonant Layered Acoustic Metamaterial (MLAM) to attenuate targeted low-frequency noise below 1000 Hz. ML generates a continuous model of RVE effective properties for sound transmission loss evaluation, enabling topology optimization via a genetic algorithm with far lower computational cost. Results extend to a higher-parameter RVE and are benchmarked against direct numerical simulation of the full 3D model.","Received: 3 November 2023 Revised: 2 February 2024 Accepted: 26 February 2024  \nDOI: 10.1002/nme.7476  \nRESEARCH ARTICLE   \nMachine learning in solid mechanics: Application to acoustic metamaterial design  \nD. Yago1,2  G. Sal-Anglada1,2  D. Roca1,2  J. Cante1,2  J. Oliver1,3  \n1 Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Barcelona, Spain  \n2 Escola Superior d’Enginyeries Industrial Aeroespacial i Audiovisuals de Terrassa (ESEIAAT), Universitat Politècnica de Catalunya–BarcelonaTech (UPC), Terrassa, Spain  \n3 Escola Tècnica Superior d’Enginyers de Camins, Canals i Ports de Barcelona (ETSECCPB), Universitat Politècnica de Catalunya–BarcelonaTech (UPC), Barcelona, Spain  \nCorrespondence  \nJ. Oliver, Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Barcelona 08034, Spain.  \nEmail: [oliver@cimne.upc.edu](oliver@cimne.upc.edu)  \nFunding information  \nSpanish Ministry of Science and Innovation, Grant/Award Numbers:  \nPRE2019-088777,  \nCEX2018-000797-S-19-1, TED2021-129413B-C21; Ministry of Research and Universities of the Government of Catalonia, Grant/Award Number: 2021-PROD-00016  \nAbstract  \nMachine learning (ML) and Deep learning (DL) are increasingly pivotal in the design of advanced metamaterials, seamlessly integrated with material or topology optimization. Their intrinsic capability to predict and interconnect material properties across vast design spaces, often computationally prohibitive for conventional methods, has led to groundbreaking possibilities. This paper introduces an innovative machine learning approach for the optimization of acoustic metamaterials, focusing on Multiresonant Layered Acoustic Metamaterial (MLAM), designed for targeted noise attenuation at low frequencies (below 1000 Hz) . This method leverages ML to create a continuous model of the Representative Volume Element (RVE) effective properties essential for evaluating sound transmission loss (STL), and subsequently used to optimize the overall topology configuration for maximum sound attenuation using a Genetic Algorithm (GA) . The significance of this methodology lies in its ability to deliver rapid results without compromising accuracy, significantly reducing the computational overhead of complete topology optimization by several orders of magnitude. To demonstrate the versatility and scalability of this approach, it is extended to a more intricate RVE model, characterized by a higher number of parameters, and is optimized using the same strategy. In addition, to underscore the potential of ML techniques in synergy with traditional topology optimization, a comparative analysis is conducted, comparing the outcomes of the proposed method with those obtained through direct numerical simulation (DNS) of the corresponding full 3D MLAM model. This comparative analysis highlights the transformative potential of this combination, particularly when addressing complex topological challenges with significant computational demands, ushering in a new era of metamaterial and component design.  \nKEYW O RDS  \nacoustic metamaterials, coupled resonances, deep-learning neural networks, genetic algorithms, machine learning, sound transmission loss, topology optimization  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n© 2024 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons Ltd.  \nInt JNumer Methods Eng. 2024;125:e7476 . [https://doi.org/10.1002/nme.7476](https://doi.org/10.1002/nme.7476)  \n[wileyonlinelibrary.com/journal/nme](wileyonlinelibrary.com/journal/nme)  \n1 of 29  \n2 of 29   YAGO et al.  \n1  MOTIVATION  \nMetamaterials have emerged in the last years as a breakthrough area of research in computational material design, exhibiting unprecedented properties compared to con","cbCaiuVLj8TfLHWU","https://ap.wps.com/l/cbCaiuVLj8TfLHWU","pdf",8242871,1,29,"English","en",105,"# Motivation\n## Acoustic metamaterials and low-frequency noise attenuation\n## Role and limits of topology optimization\n# Machine learning approach for MLAM\n## ML modeling of RVE effective properties for STL evaluation\n## Genetic algorithm topology optimization\n# Extensions and comparison study\n## More intricate RVE model with higher parameter count\n## Comparison with direct numerical simulation (DNS) of full 3D MLAM","[{\"question\":\"What optimization objective does the proposed method target in MLAM design?\",\"answer\":\"It targets maximum sound attenuation by optimizing the overall topology configuration, evaluated through sound transmission loss (STL).\"},{\"question\":\"How does the workflow use machine learning during optimization?\",\"answer\":\"ML creates a continuous model of representative volume element (RVE) effective properties, which are then used to assess STL for topology optimization.\"},{\"question\":\"Why is the proposed ML approach computationally efficient compared with conventional topology optimization?\",\"answer\":\"It avoids repeatedly performing expensive full simulations over the entire design space, enabling rapid results and reducing computational overhead by several orders of magnitude.\"}]","Machine learning in solid mechanics - Application to acoustic metamaterial design | PDF",1785820000,73,{"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-in-solid-mechanics-application-to-acoustic-metamaterial-design","",{"@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-in-solid-mechanics-application-to-acoustic-metamaterial-design/124037/",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","2026-08-04",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 optimization objective does the proposed method target in MLAM design?","Question",{"text":75,"@type":76},"It targets maximum sound attenuation by optimizing the overall topology configuration, evaluated through sound transmission loss (STL).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the workflow use machine learning during optimization?",{"text":80,"@type":76},"ML creates a continuous model of representative volume element (RVE) effective properties, which are then used to assess STL for topology optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the proposed ML approach computationally efficient compared with conventional topology optimization?",{"text":84,"@type":76},"It avoids repeatedly performing expensive full simulations over the entire design space, enabling rapid results and reducing computational overhead by several orders of magnitude.","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"]