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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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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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 |
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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. |