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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MitwirkendeR: |
Alfio, Quarteroni,
Andrea, Manzoni, Andreas, Buhr, Anthony T., Patera, Carmen, Gräßle, Charbel, Farhat, Francesco, Ballarin, Francisco, Chinesta, Gianluigi, Rozza, Giovanni, Stabile, J. Nathan, Kutz, Kathrin, Smetana, Laura, Iapichino, Marco, Tezzele, Martin, Hess, Hinze, Michael, Pierre, Ladevèze, Sebastian, Grimberg, Stefan, Volkwein, Steven L., Brunton, Yvon, Maday, |
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HerausgeberIn: | |
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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Table of Contents:
- 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
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