INTERPOLATION-BASED MODEL ORDER REDUCTION FOR POLYNOMIAL SYSTEMS

被引:19
|
作者
Benner, Peter [1 ,2 ]
Goyal, Pawan [1 ]
机构
[1] Max Planck Inst Dynam Complex Tech Syst, Sandtorstr 1, D-39106 Magdeburg, Germany
[2] Otto von Guericke Univ, Fac Math, Magdeburg, Germany
来源
SIAM JOURNAL ON SCIENTIFIC COMPUTING | 2021年 / 43卷 / 01期
关键词
model order reduction; polynomial dynamical systems; transfer functions; interpolation; tensor algebra; matricization; BILINEAR-SYSTEMS; MIMO SYSTEMS; APPROXIMATION;
D O I
10.1137/19M1259171
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this work, we investigate a model-order reduction scheme for polynomial systems. We begin with defining the generalized multivariate transfer functions for the system. Based on this, we aim at constructing a reduced-order system, interpolating the defined generalized transfer functions at a given set of interpolation points. Furthermore, we provide a method, inspired by the Loewner approach for linear and (quadratic-)bilinear systems, to determine a good-quality reduced-order system in an automatic way. We also discuss the computational issues related to the proposed method and a potential application of a CUR matrix approximation in order to further speed up simulation of the reduced-order systems. We test the efficiency of the proposed method via two benchmark examples.
引用
收藏
页码:A84 / A108
页数:25
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