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

Springer Nature - Springer Mathematics and Statistics eBooks 2024 English International Available online

Springer Nature - Springer Mathematics and Statistics eBooks 2024 English International
Format:
Author/Creator:
Contributor:
Series:
Language:
English
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Physical Description:
1 online resource (265 pages)
Edition:
1st ed. 2024.
Place of Publication:
Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
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.
Contents:
  • 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.
Notes:
Description based on publisher supplied metadata and other sources.
Other Format:
Print version: Rozza, Gianluigi Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators
ISBN:
9783031550607
OCLC:
1443082025

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