A Black-Box Method for Parametric Model Order Reduction

被引：1
|
作者：
Geuss, M. [1 ]
Lohmann, B. [1 ]
Pelterstorfer, B. [2 ]
Willcox, K. [2 ]
机构：
[1] Tech Univ Munich, Inst Automat Control, D-85748 Garching, Germany
[2] MIT, Dept Aeronaut & Astronaut, Cambridge, MA 02139 USA
来源：
IFAC PAPERSONLINE | 2015年 / 48卷 / 01期
关键词：
Parametric model order reduction; matrix interpolation cross-validation; surrogate model; method selection; model refinement; error prediction; OPTIMIZATION;
D O I：
10.1016/j.ifacol.2015.05.131
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A black-box method for parametric model order reduction is presented that includes method selection, model refinement and error prediction using a cross-validation-based error indicator. The method is demonstrated for the interpolation of reduced system matrices. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：168 / +
页数：2
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