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...
Saved in:
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. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
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. |