{"title":"Generative optimization of bistable plates with deep learning","authors":"Hong Li, Qingfeng Wang","doi":"10.1016/j.taml.2023.100483","DOIUrl":null,"url":null,"abstract":"<div><p>Bistate plates have found extensive applications in the domains of smart structures and energy harvesting devices. Most bistable curved plates are characterized by a constant thickness profile. Regrettably, due to the inherent complexity of this problem, relatively little attention has been devoted to this area. In this study, we demonstrate how deep learning can facilitate the discovery of novel plate profiles that cater to multiple objectives, including maximizing stiffness, forward snapping force, and backward snapping force. Our proposed approach is distinguished by its efficiency in terms of low computational energy consumption and high effectiveness. It holds promise for future applications in the design and optimization of multistable structures with diverse objectives, addressing the requirements of various fields.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000545/pdfft?md5=39d4d8735e9e88727c8be23fdd5bfb70&pid=1-s2.0-S2095034923000545-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical and Applied Mechanics Letters","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2095034923000545","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
引用次数: 0
Abstract
Bistate plates have found extensive applications in the domains of smart structures and energy harvesting devices. Most bistable curved plates are characterized by a constant thickness profile. Regrettably, due to the inherent complexity of this problem, relatively little attention has been devoted to this area. In this study, we demonstrate how deep learning can facilitate the discovery of novel plate profiles that cater to multiple objectives, including maximizing stiffness, forward snapping force, and backward snapping force. Our proposed approach is distinguished by its efficiency in terms of low computational energy consumption and high effectiveness. It holds promise for future applications in the design and optimization of multistable structures with diverse objectives, addressing the requirements of various fields.
期刊介绍：
An international journal devoted to rapid communications on novel and original research in the field of mechanics. TAML aims at publishing novel, cutting edge researches in theoretical, computational, and experimental mechanics. The journal provides fast publication of letter-sized articles and invited reviews within 3 months. We emphasize highlighting advances in science, engineering, and technology with originality and rapidity. Contributions include, but are not limited to, a variety of topics such as: • Aerospace and Aeronautical Engineering • Coastal and Ocean Engineering • Environment and Energy Engineering • Material and Structure Engineering • Biomedical Engineering • Mechanical and Transportation Engineering • Civil and Hydraulic Engineering Theoretical and Applied Mechanics Letters (TAML) was launched in 2011 and sponsored by Institute of Mechanics, Chinese Academy of Sciences (IMCAS) and The Chinese Society of Theoretical and Applied Mechanics (CSTAM). It is the official publication the Beijing International Center for Theoretical and Applied Mechanics (BICTAM).
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