通过经验格兰方法减少优化系统的模型阶次

IF 1.3 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Frontiers in Applied Mathematics and Statistics Pub Date : 2023-12-11 DOI:10.3389/fams.2023.1144142
Luca Mechelli, Jan Rohleff, Stefan Volkwein
{"title":"通过经验格兰方法减少优化系统的模型阶次","authors":"Luca Mechelli, Jan Rohleff, Stefan Volkwein","doi":"10.3389/fams.2023.1144142","DOIUrl":null,"url":null,"abstract":"In the present article, optimal control problems for linear parabolic partial differential equations (PDEs) with time-dependent coefficient functions are considered. One of the common approach in literature is to derive the first-order sufficient optimality system and to apply a finite element (FE) discretization. This leads to a specific linear but high-dimensional time variant (LTV) dynamical system. To reduce the size of the LTV system, we apply a tailored reduced order modeling technique based on empirical gramians and derived directly from the first-order optimality system. For testing purpose, we focus on two specific examples: a multiobjective optimization and a closed-loop optimal control problem. Our proposed methodology results to be better performing than a standard proper orthogonal decomposition (POD) approach for the above mentioned examples.","PeriodicalId":36662,"journal":{"name":"Frontiers in Applied Mathematics and Statistics","volume":"11 2","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Model order reduction for optimality systems through empirical gramians\",\"authors\":\"Luca Mechelli, Jan Rohleff, Stefan Volkwein\",\"doi\":\"10.3389/fams.2023.1144142\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the present article, optimal control problems for linear parabolic partial differential equations (PDEs) with time-dependent coefficient functions are considered. One of the common approach in literature is to derive the first-order sufficient optimality system and to apply a finite element (FE) discretization. This leads to a specific linear but high-dimensional time variant (LTV) dynamical system. To reduce the size of the LTV system, we apply a tailored reduced order modeling technique based on empirical gramians and derived directly from the first-order optimality system. For testing purpose, we focus on two specific examples: a multiobjective optimization and a closed-loop optimal control problem. Our proposed methodology results to be better performing than a standard proper orthogonal decomposition (POD) approach for the above mentioned examples.\",\"PeriodicalId\":36662,\"journal\":{\"name\":\"Frontiers in Applied Mathematics and Statistics\",\"volume\":\"11 2\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Applied Mathematics and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fams.2023.1144142\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Applied Mathematics and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fams.2023.1144142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 1

摘要

本文考虑的是具有随时间变化的系数函数的线性抛物线偏微分方程(PDE)的最优控制问题。文献中常见的方法之一是推导一阶充分最优化系统并应用有限元(FE)离散化。这就产生了一个特定的线性但高维的时间变量(LTV)动力系统。为了减小 LTV 系统的规模,我们采用了一种基于经验格兰的定制减阶建模技术,并直接从一阶最优化系统中推导出来。为了测试目的,我们重点讨论了两个具体例子:多目标优化和闭环最优控制问题。在上述例子中,我们提出的方法比标准的适当正交分解(POD)方法性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Model order reduction for optimality systems through empirical gramians
In the present article, optimal control problems for linear parabolic partial differential equations (PDEs) with time-dependent coefficient functions are considered. One of the common approach in literature is to derive the first-order sufficient optimality system and to apply a finite element (FE) discretization. This leads to a specific linear but high-dimensional time variant (LTV) dynamical system. To reduce the size of the LTV system, we apply a tailored reduced order modeling technique based on empirical gramians and derived directly from the first-order optimality system. For testing purpose, we focus on two specific examples: a multiobjective optimization and a closed-loop optimal control problem. Our proposed methodology results to be better performing than a standard proper orthogonal decomposition (POD) approach for the above mentioned examples.
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
Frontiers in Applied Mathematics and Statistics
Frontiers in Applied Mathematics and Statistics Mathematics-Statistics and Probability
CiteScore
1.90
自引率
7.10%
发文量
117
审稿时长
14 weeks
期刊最新文献
Third-degree B-spline collocation method for singularly perturbed time delay parabolic problem with two parameters Item response theory to discriminate COVID-19 knowledge and attitudes among university students Editorial: Justified modeling frameworks and novel interpretations of ecological and epidemiological systems Pneumonia and COVID-19 co-infection modeling with optimal control analysis Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1