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

• MitwirkendeR: Benner, Peter,; Feng, Lihong,; Gosea, Ion Victor,; Grivet-Talocia, Stefano,; Haasdonk, Bernard,; Karachalios, Dimitrios S.,; Kleijnen, Jack P. C.,; Magdeburg, Luìs Miguel Silveira,; Mahdiabadi, Morteza Karamooz,; Quarteroni, Alfio,; Rozza, Gianluigi,; Santin, Gabriele,; Schilders, W. H. A.; Silveira, Luis Miguel,; Silveira, Luís Miguel,; Tiso, Paolo,; Triverio, Piero,; Zimmermann, Ralf,
• HerausgeberIn: Benner, Peter,; Grivet-Talocia, Stefano,; Quarteroni, Alfio,; Rozza, Gianluigi,; Schilders, W. H. A.; Silveira, Luís Miguel,
• 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.

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...

Table of Contents

• 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