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Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators : RAMSES / edited by Gianluigi Rozza, Giovanni Stabile, Max Gunzburger, Marta D'Elia

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Lecture Notes in Computational Science and Engineering ; 151Publisher: Cham : Springer Nature Switzerland, 2024Publisher: Cham : Imprint: Springer, 2024Edition: 1st ed. 2024Description: 1 Online-Ressource(X, 259 p. 151 illus., 148 illus. in color.)ISBN:
  • 9783031550607
Subject(s): Genre/Form: Additional physical formats: 9783031550591 | 9783031550614 | 9783031550621 | Erscheint auch als: 9783031550591 Druck-Ausgabe | Erscheint auch als: 9783031550614 Druck-Ausgabe | Erscheint auch als: 9783031550621 Druck-Ausgabe | Erscheint auch als: Reduction, approximation, machine learning, surrogates, emulators and simulators. Druck-Ausgabe Cham : Springer, 2024. x, 259 SeitenDDC classification:
  • 518 23
DOI: DOI: 10.1007/978-3-031-55060-7Online resources: Summary: Shafqat Ali, Francesco Ballarin and Gianluigi Rozza: An online stabilization method for parametrized viscous flows -- Margarita Chasapi, Pablo Antolin, Annalisa Buffa: Reduced order modelling of nonaffine problems on parameterized NURBS multipatch geometries -- Anton Dereventsov, Joseph Daws, Jr., and Clayton G. Webster: Offline Policy Comparison under Limited Historical Agent-Environment Interactions -- Julien Genovese, Francesco Ballarin, Gianluigi Rozza and Claudio Canuto: Weighted reduced order methods for uncertainty quantification in computational fluid dynamics.Summary: This volume is focused on the review of recent algorithmic and mathematical advances and the development of new research directions for Mathematical Model Approximations via RAMSES (Reduced order models, Approximation theory, Machine learning, Surrogates, Emulators, Simulators) in the setting of parametrized partial differential equations also with sparse and noisy data in high-dimensional parameter spaces. The book is a valuable resource for researchers, as well as masters and Ph.D students.PPN: PPN: 1893108368Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS
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