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Record Nr. |
UNINA9910866581503321 |
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Autore |
Rozza Gianluigi |
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Titolo |
Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators : RAMSES / / edited by Gianluigi Rozza, Giovanni Stabile, Max Gunzburger, Marta D'Elia |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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9783031550607 |
9783031550591 |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (265 pages) |
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Collana |
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Lecture Notes in Computational Science and Engineering, , 2197-7100 ; ; 151 |
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Altri autori (Persone) |
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StabileGiovanni |
GunzburgerMax |
D'EliaMarta |
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Disciplina |
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Soggetti |
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Numerical analysis |
Machine learning |
Numerical Analysis |
Machine Learning |
Aprenentatge automàtic |
Teoria de l'aproximació |
Models matemàtics |
Equacions en derivades parcials |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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. |
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