: Hans-Georg Bock, Frank Hoog, Avner Friedman, Arvind Gupta, Helmut Neunzert, William R. Pulleyblank
: Wilhelmus H. Schilders, Henk A. van der Vorst, Joost Rommes
: Model Order Reduction: Theory, Research Aspects and Applications
: Springer-Verlag
: 9783540788416
: 1
: CHF 132.90
:
: Arithmetik, Algebra
: English
: 466
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF
The idea for this book originated during the workshop 'Model order reduction, coupled problems and optimization' held at the Lorentz Center in Leiden from S- tember 19-23, 2005. During one of the discussion sessions, it became clear that a book describing the state of the art in model order reduction, starting from the very basics and containing an overview of all relevant techniques, would be of great use for students, young researchers starting in the ?eld, and experienced researchers. The observation that most of the theory on model order reduction is scattered over many good papers, making it dif?cult to ?nd a good starting point, was supported by most of the participants. Moreover, most of the speakers at the workshop were willing to contribute to the book that is now in front of you. The goal of this book, as de?ned during the discussion sessions at the workshop, is three-fold: ?rst, it should describe the basics of model order reduction. Second, both general and more specialized model order reduction techniques for linear and nonlinear systems should be covered, including the use of several related numerical techniques. Third, the use of model order reduction techniques in practical appli- tions and current research aspects should be discussed. We have organized the book according to these goals. In Part I, the rationale behind model order reduction is explained, and an overview of the most common methods is described.

Wil Schilders received the MSc degree in pure and applied mathematics from Nijmegen University in 1978, and a PhD in numerical analysis from Trinity College Dublin in 1980. Since 1980, he has been with Philips Electronics, where he developed algorithms for semiconductor device simulation, electronic circuit simulation, and electromagnetics problems. He wrote two volumes on the numerical simulation of semiconductor devices, and published a special volume on Numerical Methods in Electromagnetics. Since 1999, he is part-time professor in numerical analysis for industry at Eindhoven University of Technology. He developed a novel method known as the Schilders factorization for the solution of indefinite linear systems. Since more than a decade, his interest is in model order reduction, and he is a frequent organizer of workshops and symposia on this topic. Currently, he is with NXP Semiconductors, heading the Mathematics group.

Henk van der Vorst is a leading expert in numerical linear algebra, in particular in iterative methods for linear systems and eigenproblems. The techniques developed and used in these areas are of very high interest in model order reduction. Van der Vorst was the (co-) author of novel and highly important techniques, including incomplete decompositions, Bi-CGSTAB, and the Jacobi-Davidson method. The Bi-CGSTAB paper was the most cited paper in mathematics  of the 1990's according to ISI in 2000. For the Jacobi-Davidson method he received, together with co-author Sleijpen  a SIAG-LA Award for the best paper in numerical linear algebra over a three year period. Van der Vorst is Editor in Chief of the SIAM Journal SIMAX and he is member of the Netherlands Royal Academy of Sciences.

Joost Rommes received the M.Sc. degree in computational science, the M.Sc.
degree in computer science, and the Ph.D. degree in mathematics from Utrecht
University, Utrecht, The Netherlands, in 2002, 2003, and 2007, respectively. During his PhD studies he worked on eigensolution methods with applications in model order reduction. Some of his developed methods are now used in software for circuit simulation and power system analysis. Joost Rommes currently works at NXP Semiconductors on model order reduction. In the electronics industry, an increase in complexity at transistor level leads to much large models that can not be simulated without accurate reduction techniques. Due to specific properties of the models, there is also need for different reduction techniques that can deal with these properties. This book provides a wide range of reduction techniques.

Preface5
Contents7
List of Contributors9
Part I Basic Concepts13
Introduction to Model Order Reduction14
1 Introduction14
2 Transfer Function, Stability and Passivity23
3 A Short Account of Techniques for Model Order Reduction29
References42
Linear Systems, Eigenvalues, and Projection44
1 Introduction44
2 Linear Systems48
3 Subspace Methods49
References55
Part II Theory58
Structure-Preserving Model Order Reduction of RCL Circuit Equations60
1 Introduction60
2 Formulation of General RCL Circuits as Integro-DAEs62
3 Structure-Preserving Model Order Reduction65
4 Equivalent First-Order Form of Integro-DAEs68
5 Krylov-Subspace Projection and PRIMA72
6 The SPRIM Algorithm74
7 Pade-Type Approximation Property of SPRIM77
8 Numerical Examples78
9 Concluding Remarks81
Acknowledgement82
References82
A Unified Krylov Projection Framework for Structure- Preserving Model Reduction86
1 Introduction86
2 A unified Krylov Projection Structure-Preserving Model Order Reduction Framework87
3 Structure of Krylov Subspace and Arnoldi Process93
4 RCL and RCS Systems94
References103
Model Reduction via Proper Orthogonal Decomposition106
1 Introduction106
2 Proper Orthogonal Decomposition107
3 POD in Radiative Heat Transfer113
4 Conclusions and Future Perspectives116
Acknowledgments118
References118
PMTBR: A Family of Approximate Principal- components- like Reduction Algorithms122
1 Introduction122
2 Basic Algorithm124
3 Algorithmic Variants128
4 Analysis and Comparisons134
5 Experimental Results136
6 Conclusions141
References142
A Survey on Model Reduction of Coupled Systems.144
1 Introduction144
2 Coupled Systems145
3 Model Reduction Approaches for Coupled Systems148
4 Numerical Examples157
References164
Space Mapping and Defect Correction168
1 Introduction168
2 Fine and Coarse Models in Optimization169
3 Space-Mapping Optimization171
4 Defect Correction and Space Mapping175
5 Manifold Mapping, the Improved Space Mapping Algorithm178
6 Examples181
7 Conclusions186
References186
Modal Approximation and Computation of Dominant Poles188
1 Introduction188
2 Transfer Functions, Dominant Poles and Modal Equivalents188
3 Computing Dominant Poles190
4 Generalizations197
5 Numerical Examples199
6 Conclusions203
Acknowledgement203
References203
Some Preconditioning Techniques for Saddle Point Problems206
1 Introduction206
2 Properties of Saddle Point Systems207
3 Preconditioned Krylov Subspace Methods208
4 Block Preconditioners210
5 Augmented Lagrangian Formulations212
6 Constraint Preconditioning213
7 Other Techniques216
8 Numerical Examples217
9 Conclusions219
References220
Time Variant Balancing and Nonlinear Balanced Realizations224
1 Introduction224
2 Time Varying Linear Systems225
3 Sliding Interval Balancing230
4 Nonlinear Balancing235
5 Global Balancing250
6 Mayer-Lie Interpolation253
7 Nonlinear Model Reduction255
8 How Far Can You Go?256
9 Conclusions258
References259
Singular Value Analysis and Balanced Realizations for Nonlinear Systems262
1 Introduction262
2 Singular Value Analysis of Nonlinear Operators263
3 Balanced Realization for Linear Systems266
4 Basics of Nonlinear Balanced Realizations269
5 Balanced Realizations Based on Singular Value Analysis of Hankel Operators273
6 Model Order Reduction276
7 Numerical Example278
8 Conclusion282
References282
Part III Research Aspects and Applications284
Matrix Functions286
1 Introduction286
2 Matrix Functions286
3 Computational Aspects289
4 The Exponential Function296
5 The Matrix Sign Function304
References311
Model Reduction of Interconnected Systems316
1 Introduction316