|Statement||Wilhelmus H.A. Schilders, Henk A. van der Vorst, Joost Rommes, editors|
|Series||Mathematics in industry -- 13. -- The European Consortium for Mathematics in Industry, Mathematics in industry -- 13., European Consortium for Mathematics in Industry (Series)|
|LC Classifications||TA347.L5 M63 2008|
|The Physical Object|
|Pagination||xi, 471 p. :|
|Number of Pages||471|
|ISBN 10||9783540788409, 9783540788416|
|LC Control Number||2008929907|
Reduced-order models are neither robust with respect to parameter changes nor cheap to generate. A method based on a database of ROMs coupled with a suitable interpolation schemes greatly reduces the computational cost for aeroelastic predictions while retaining good accuracy. Model reduction can also ameliorate problems in the correlation of widely used finite-element analyses and test analysis models produced by excessive system complexity. Model Order Reduction Techniques explains and compares such methods focusing mainly on Cited by: This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational. Model (Order) Reduction • ~1 hits in Google • , , • Many different research communities use different forms of model reduction: Fluid dynamics Mechanics Computational biology Circuit design Control theory • Many heuristics available. More or less well-motivated.
Model order reduction aims to lower the computational complexity of such problems, for example, in simulations of large-scale dynamical systems and control systems. By a reduction of the model's associated state space dimension or degrees of freedom, an approximation to the original model is computed which is commonly referred to as a reduced. Model Order Reduction Model reduction or model order reduction is a mathematical theory to find a low-dimensional approximation for a system of ordinary differential equations (ODEs). The main idea is that a high-dimensional state vector is actually belongs to a low-dimensional subspace as shown in Fig. 1. Introduction to Model Order Reduction Wil Schilders1,2 1 NXP Semiconductors, Eindhoven, The Netherlands [email protected] 2 Eindhoven University of Technology, Faculty of Mathematics and Computer Science, Eindhoven, The Netherlands [email protected] 1 Introduction In this ﬁrst section we present a high level discussion on computational science, andCited by: of model order reduction, G can be represented as a series connection of a lower order “nominal plant” Gˆ and a bounded uncertainty (see Figure ). In most cases, (even when W ≥ 1) the H-Inﬁnity optimal model order reduction is a problem without a known good Size: KB.
able to replace (approximate) these models by simpler models with reduced order. In this process it is important to design the reduced model so as to capture the important properties of the original high-order model. This chapter describes some procedures that are available for the model reduction of linear time-invariant systems. 1. IntroductionFile Size: KB. Model order reduction is an important tool in control systems theory. In particular, it is useful for controller design since the dimension of the controller becomes very high when we use advanced Author: Janardhanan Sivaramakrishnan. G is a 48th-order model with several large peak regions around rad/s, rad/s, and rad/s, and smaller peaks scattered across many frequencies. Suppose that for your application you are only interested in the dynamics near the second large peak, between 10 rad/s and 22 rad/s. Focus the model reduction on the region of interest to obtain a good match with a low-order approximation. Model order reduction for linear state-space systems has been a topic of research for about 50 years at the time of writing, and by now can be considered as a mature field. This book deals.