Model Order Reduction. / Volume 1, : System- and Data-Driven Methods and Algorithms / / ed. by Peter Benner.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real...

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Bibliographic Details
MitwirkendeR:
HerausgeberIn:
Place / Publishing House:Berlin ;, Boston : : De Gruyter,, [2021]
©2021
Year of Publication:2021
Language:English
Series:Model Order Reduction ; Volume 1
Physical Description:1 online resource (X, 378 p.)
Notes:Description based upon print version of record.
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Description
Other title:Frontmatter --
Preface to the first volume of Model Order Reduction --
Contents --
1 Model order reduction: basic concepts and notation --
2 Balancing-related model reduction methods --
3 Model order reduction based on moment-matching --
4 Modal methods for reduced order modeling --
5 Post-processing methods for passivity enforcement --
6 The Loewner framework for system identification and reduction --
7 Manifold interpolation --
8 Vector fitting --
9 Kernel methods for surrogate modeling --
10 Kriging: methods and applications --
Index
Summary:An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.
ISBN:3110498960
Hierarchical level:Monograph
Statement of Responsibility: ed. by Peter Benner.