Model Order Reduction. / Volume 2, : Snapshot-Based Methods and Algorithms / / Peter Benner, Wil Schilders, Stefano Grivet-Talocia, Alfio Quarteroni, Gianluigi Rozza, Luís Miguel Silveira.

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 second volume focuses on app...

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Bibliographic Details
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Place / Publishing House:Berlin ;, Boston : : De Gruyter,, [2020]
©2021
Year of Publication:2020
Language:English
Series:Model Order Reduction ; Volume 2
Physical Description:1 online resource (VIII, 348 p.)
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Description
Other title:Frontmatter --
Preface to the second volume of Model Order Reduction --
Contents --
1 Basic ideas and tools for projection-based model reduction of parametric partial differential equations --
2 Model order reduction by proper orthogonal decomposition --
3 Proper generalized decomposition --
4 Reduced basis methods --
5 Computational bottlenecks for PROMs: precomputation and hyperreduction --
6 Localized model reduction for parameterized problems --
7 Data-driven methods for reduced-order modeling --
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 second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.
ISBN:3110671492
Hierarchical level:Monograph
Statement of Responsibility: Peter Benner, Wil Schilders, Stefano Grivet-Talocia, Alfio Quarteroni, Gianluigi Rozza, Luís Miguel Silveira.