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Reduction, approximation, machine learning, surrogates, emulators and simulators = RAMSES /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reduction, approximation, machine learning, surrogates, emulators and simulators/ edited by Gianluigi Rozza ... [et al.].
Reminder of title:	RAMSES /
other author:	Rozza, Gianluigi.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	x, 259 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Approximation theory. -
Online resource:	https://doi.org/10.1007/978-3-031-55060-7
ISBN:	9783031550607

Reduction, approximation, machine learning, surrogates, emulators and simulators = RAMSES /
Reduction, approximation, machine learning, surrogates, emulators and simulatorsRAMSES /[electronic resource] :edited by Gianluigi Rozza ... [et al.]. - Cham :Springer Nature Switzerland :2024. - x, 259 p. :ill. (chiefly col.), digital ;24 cm. - Lecture notes in computational science and engineering,v. 1512197-7100 ;. - Lecture notes in computational science and engineering ;v. 151..

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.

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.

ISBN: 9783031550607

Standard No.: 10.1007/978-3-031-55060-7doiSubjects--Topical Terms:

628068
Approximation theory.

LC Class. No.: QA221

Dewey Class. No.: 511.4

Reduction, approximation, machine learning, surrogates, emulators and simulators = RAMSES /
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490 1 $a Lecture notes in computational science and engineering, $x 2197-7100 ; $v v. 151
505 0 $a 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.
520 $a 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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