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Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators RAMSES /

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 par...

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Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Rozza, Gianluigi (Editor), Stabile, Giovanni (Editor), Gunzburger, Max (Editor), D'Elia, Marta (Editor)
Formato: Electrónico eBook
Lenguaje:inglés
Publicado: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Edición:1st ed. 2024.
Colección:Lecture Notes in Computational Science and Engineering, 151
Materias:
Numerical analysis.
Machine learning.
Numerical Analysis.
Machine Learning.
Acceso en línea:https://doi.org/10.1007/978-3-031-55060-7
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Descripción
Sumario: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.
Descripción Física:1 online resource (X, 259 p. 151 illus.). 148 illus. in color.
ISBN:9783031550607
ISSN:2197-7100 ;
DOI:10.1007/978-3-031-55060-7

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