[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119188-en":3,"doc-seo-119188-105":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},119188,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning and deep learning in phononic crystals and metamaterials - A review - State-of-the-art survey","Machine learning (ML) and deep learning (DL) are leveraged to accelerate structural design exploration for phononic crystals and metamaterials, complementing physics-based and formulation-driven approaches with data-driven methodologies. The review summarizes historical context, network architectures, and working principles used in recent research to analyze and optimize artificial acoustic and mechanical structures. It explains how these learning architectures support design and optimization tasks, while covering future prospects for this multidisciplinary and fast-evolving field and updating acoustics, mechanics, physics, and material science communities.","| Title | Machine learning and deep learning in phononic crystals and metamaterials – A review |\n| --- | --- |\n| Authors(s) | Gulzari, Muhammad, Kennedy, John, Lim, C. W. |\n| Publication date | 2022-12 |\n| Publication information | Gulzari, Muhammad, John Kennedy, and C. W. Lim.“Machine Learning and Deep Learning in Phononic Crystals and Metamaterials – A Review” 33 (December, 2022) . |\n| Publisher | Elsevier |\n| Item record/more\u003Cbr>information | [http://hdl.handle.net/10197/26738](http://hdl.handle.net/10197/26738) |\n| Publisher's statement | This is the author’s version of a work that was accepted for publication in Materials Today Communications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Materials Today Communications (33, Article Number: 104606,(2022)) DOI: [https://doi.org/10.1016/j.mtcomm.2022.104606](https://doi.org/10.1016/j.mtcomm.2022.104606) |\n| Publisher's version (DOI) | 10.1016/j.mtcomm.2022.104606 |\n\nDownloaded 2024-10-19 11:59:18  \nThe UCD community has made this article openly available. Please share how this access  \nbenefits you. Your story matters! (@ucd_oa)  \n© Some rights reserved. For more information  \nMachine Learning and Deep Learning in Phononic Crystals and  \nMetamaterials ‒ A Review  \nMuhammad 1*, John Kennedy 1 and C.W. Lim2  \n1Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, College Green, Dublin 02, D02 PN40, Ireland  \n2Department of Architecture and Civil Engineering, City University of Hong Kong, Tat Che Avenue, Kowloon, Hong Kong SAR, P.R. China  \nAbstract  \nMachine learning (ML), as a component of artificial intelligence, encourages structural design exploration which leads to new technological advancements. By developing and generating data-driven methodologies that supplement conventional physics and formulabased approaches, deep learning (DL), a subset of machine learning offers an efficient way to understand and harness artificial materials and structures. Recently, acoustic and mechanics communities have observed a surge of research interest in implementing machine learning and deep learning methods in the design and optimization of artificial materials. In this review we evaluate the recent developments and present a state-of-the-art literature survey in machine learning and deep learning based phononic crystals and metamaterial designs by giving historical context, discussing network architectures and working principles. We also explain the application of these network architectures adopted for design and optimization of artificial structures. Since this multidisciplinary research field is evolving, a summary of the future prospects is also covered. This review article serves to update the acoustics, mechanics, physics, material science and deep learning communities about the recent developments in this newly emerging research direction.  \nKeywords: acoustic metamaterial, deep learning, machine learning, mechanical metamaterials, phononic crystal  \n* Corresponding author email ([Dr.Muhammad@tcd.ie/ fmuhammad6-c@my.cityu.edu.hk](Dr.Muhammad@tcd.ie/ fmuhammad6-c@my.cityu.edu.hk))  \n1. General Introduction  \nPhononic crystals (PnCs) and metamaterials have emerged as the potential candidates for acoustic and elastic wave manipulation due to their structure-dependent peculiar wave dispersion and dynamic characteristics that are unattainable from natural materials. These fantastic wave phenomena are observed in the frequency bandgap (BG) regions where wave propagation is restricted. The frequency of propagating waves lying inside such BG cannot propagate through these periodic or aperiodic synthetic structures. Thus, it provides a favourable platform to manipulate the inciden","cbCaiqUvVjslRPxL","https://ap.wps.com/l/cbCaiqUvVjslRPxL","pdf",2937490,1,49,"English","en",105,"# General Introduction\n## Phononic crystals and metamaterials: wave manipulation and frequency bandgaps\n## Key distinction between phononic crystals and acoustic metamaterials (Bragg vs local resonance)","[{\"question\":\"What role do machine learning and deep learning play in designing phononic crystals and metamaterials?\",\"answer\":\"They enable structural design exploration using data-driven methodologies that supplement conventional physics-based and formula-based approaches.\"},{\"question\":\"What is a frequency bandgap (BG) and why is it important for these materials?\",\"answer\":\"The BG is a frequency region where wave propagation is restricted, preventing waves within that range from transmitting through periodic or aperiodic synthetic structures, enabling wave manipulation.\"},{\"question\":\"How do phononic crystals differ from acoustic metamaterials in generating bandgaps?\",\"answer\":\"Phononic crystals rely on periodicity and Bragg scattering, while acoustic metamaterials use resonators and wave hybridization to create subwavelength BG via local resonance mechanisms.\"}]","Machine learning and deep learning in phononic crystals and metamaterials - A review - State-of-the-art survey | PDF",1785723006,123,{"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-and-deep-learning-in-phononic-crystals-and-metamaterials-a-review-state-of-the-art-survey","",{"@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-and-deep-learning-in-phononic-crystals-and-metamaterials-a-review-state-of-the-art-survey/119188/",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","2026-08-03",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 role do machine learning and deep learning play in designing phononic crystals and metamaterials?","Question",{"text":75,"@type":76},"They enable structural design exploration using data-driven methodologies that supplement conventional physics-based and formula-based approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is a frequency bandgap (BG) and why is it important for these materials?",{"text":80,"@type":76},"The BG is a frequency region where wave propagation is restricted, preventing waves within that range from transmitting through periodic or aperiodic synthetic structures, enabling wave manipulation.",{"name":82,"@type":73,"acceptedAnswer":83},"How do phononic crystals differ from acoustic metamaterials in generating bandgaps?",{"text":84,"@type":76},"Phononic crystals rely on periodicity and Bragg scattering, while acoustic metamaterials use resonators and wave hybridization to create subwavelength BG via local resonance mechanisms.","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"]