Model order reduction for optimality systems through empirical gramians

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
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引用次数: 1

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.
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通过经验格兰方法减少优化系统的模型阶次
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。
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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
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