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[null,"1.","Record Nr.",null,"UNISA996398645003316"]
[null,null,"Autore",null,"Benner Peter"]
[null,null,"Titolo",null,"Model Order Reduction . Volume 2 Snapshot-Based Methods and Algorithms / / Peter Benner, Wil Schilders, Stefano Grivet-Talocia, Alfio Quarteroni, Gianluigi Rozza, Luís Miguel Silveira"]
[null,null,"Pubbl/distr/stampa",null,"Berlin/Boston, : De Gruyter, 2020"]
[{"$nulls":4},"Berlin ; ; Boston : , : De Gruyter, , [2020]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-11-067149-2"]
[null,null,"Descrizione fisica",null,"1 online resource (VIII, 348 p.)"]
[null,null,"Collana",null,"Model Order Reduction ; ; Volume 2"]
[null,null,"Disciplina",null,"515.353"]
[null,null,"Soggetti",null,"MATHEMATICS / Numerical Analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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"]
[null,null,null,"Sommario/riassunto","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."]