Model Order Reduction. Volume 2, Snapshot-Based Methods and Algorithms

Model Order Reduction Volume 2, Snapshot-Based Methods and Algorithms Volume 2, Snapshot-Based Methods and Algorithms /

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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Detalles Bibliográficos
Otros Autores: Benner, Peter (Editor), Alfio, Quarteroni, contributor (contributor), Andrea, Manzoni, contributor (editor), Andreas, Buhr, contributor, Anthony T., Patera, contributor, Benner, Peter, editor, Carmen, Gräßle, contributor, Charbel, Farhat, contributor, Francesco, Ballarin, contributor, Francisco, Chinesta, contributor, Gianluigi, Rozza, contributor, Giovanni, Stabile, contributor, Grivet-Talocia, Stefano, editor, J. Nathan, Kutz, contributor, Kathrin, Smetana, contributor, Laura, Iapichino, contributor, Marco, Tezzele, contributor, Martin, Hess, contributor, Hinze, Michael, contributor, Pierre, Ladevèze, contributor, Quarteroni, Alfio, editor, Rozza, Gianluigi, editor, Schilders, W. H. A. (Wil), editor, Sebastian, Grimberg, contributor, Silveira, Luís Miguel, editor, Stefan, Volkwein, contributor, Steven L., Brunton, contributor, Yvon, Maday, contributor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berlin/Boston De Gruyter 2020
Berlin ; Boston : [2020]
Colección:Model Order Reduction ; Volume 2
Materias:
MATHEMATICS / Numerical Analysis.
Applied mathematics
PROM
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009429259006719
Tabla de Contenidos:
  • 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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