sBF-BO-2CoGP:用于设计应用的顺序双保真约束贝叶斯优化

Anh Tran, T. Wildey, S. McCann
{"title":"sBF-BO-2CoGP:用于设计应用的顺序双保真约束贝叶斯优化","authors":"Anh Tran, T. Wildey, S. McCann","doi":"10.1115/detc2019-97986","DOIUrl":null,"url":null,"abstract":"\n Bayesian optimization is an effective surrogate-based optimization method that has been widely used for simulation-based applications. However, the traditional Bayesian optimization (BO) method is only applicable to single-fidelity applications, whereas multiple levels of fidelity exist in reality. In this work, we propose a bi-fidelity known/unknown constrained Bayesian optimization method for design applications. The proposed framework, called sBF-BO-2CoGP, is built on a two-level CoKriging method to predict the objective function. An external binary classifier, which is also another CoKriging model, is used to distinguish between feasible and infeasible regions. The sBF-BO-2CoGP method is demonstrated using a numerical example and a flip-chip application for design optimization to minimize the warpage deformation under thermal loading conditions.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"sBF-BO-2CoGP: A Sequential Bi-Fidelity Constrained Bayesian Optimization for Design Applications\",\"authors\":\"Anh Tran, T. Wildey, S. McCann\",\"doi\":\"10.1115/detc2019-97986\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Bayesian optimization is an effective surrogate-based optimization method that has been widely used for simulation-based applications. However, the traditional Bayesian optimization (BO) method is only applicable to single-fidelity applications, whereas multiple levels of fidelity exist in reality. In this work, we propose a bi-fidelity known/unknown constrained Bayesian optimization method for design applications. The proposed framework, called sBF-BO-2CoGP, is built on a two-level CoKriging method to predict the objective function. An external binary classifier, which is also another CoKriging model, is used to distinguish between feasible and infeasible regions. The sBF-BO-2CoGP method is demonstrated using a numerical example and a flip-chip application for design optimization to minimize the warpage deformation under thermal loading conditions.\",\"PeriodicalId\":352702,\"journal\":{\"name\":\"Volume 1: 39th Computers and Information in Engineering Conference\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 1: 39th Computers and Information in Engineering Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/detc2019-97986\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 1: 39th Computers and Information in Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/detc2019-97986","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

摘要

贝叶斯优化是一种有效的基于代理的优化方法,已广泛应用于基于仿真的应用中。然而,传统的贝叶斯优化(BO)方法仅适用于单保真度应用,而现实中存在多级保真度。在这项工作中,我们提出了一种双保真已知/未知约束贝叶斯优化方法用于设计应用。所提出的框架,称为sBF-BO-2CoGP,是建立在一个两级CoKriging方法来预测目标函数。外部二元分类器(也是另一种CoKriging模型)用于区分可行和不可行区域。通过一个数值算例和一个倒装芯片应用,对sBF-BO-2CoGP方法进行了设计优化,以最小化热载荷条件下的翘曲变形。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
sBF-BO-2CoGP: A Sequential Bi-Fidelity Constrained Bayesian Optimization for Design Applications
Bayesian optimization is an effective surrogate-based optimization method that has been widely used for simulation-based applications. However, the traditional Bayesian optimization (BO) method is only applicable to single-fidelity applications, whereas multiple levels of fidelity exist in reality. In this work, we propose a bi-fidelity known/unknown constrained Bayesian optimization method for design applications. The proposed framework, called sBF-BO-2CoGP, is built on a two-level CoKriging method to predict the objective function. An external binary classifier, which is also another CoKriging model, is used to distinguish between feasible and infeasible regions. The sBF-BO-2CoGP method is demonstrated using a numerical example and a flip-chip application for design optimization to minimize the warpage deformation under thermal loading conditions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信