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Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics (Computational Science and Engineering) - Tapa blanda
Sinopsis
Reduced order modeling is an important, growing field in computational science and engineering, and this is the first book to address the subject in relation to computational fluid dynamics. It focuses on complex parametrization of shapes for their optimization and includes recent developments in advanced topics such as turbulence, stability of flows, inverse problems, optimization, and flow control, as well as applications.
This book will be of interest to researchers and graduate students in the field of reduced order modeling.
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Acerca del autor
Gianluigi Rozza is a full professor of Numerical Analysis and Scientific Computing at SISSA MathLab – International School for Advanced Studies, in Trieste, Italy. His research interests include numerical analysis, numerical simulation, scientific computing, and reduced order modeling and methods with a special focus on viscous flows and complex geometrical parametrizations.
Giovanni Stabile is an assistant professor at SISSA MathLab – International School for Advanced Studies, in Trieste, Italy. His current research interests include several aspects concerning the numerical approximation of partial differential equations with a special emphasis on model order reduction techniques for computational fluid dynamic problems. He has worked with industrial partners on a variety of projects aimed at exporting advanced numerical methods to the industrial world.
Francesco Ballarin is an assistant professor in Numerical Analysis in the Department of Mathematics and Physics at Catholic University of the Sacred Heart, Brescia, Italy. His research focuses on reduced order models for parametrized problems in computational fluid dynamics and applications in engineering and life sciences, such as those arising from environmental problems or patient-specific mathematical modeling of the cardiovascular system. He also develops open source software libraries.
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Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics (Computational Science and Engineering)
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SIAM - Society for Industrial and Applied Mathematics, 2022
ISBN 10: 161197724X
ISBN 13: 9781611977240
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Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics
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SIAM - Society for Industrial and Applied Mathematics, 2022
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ISBN 13: 9781611977240
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Paperback. Condición: new. Paperback. Reduced order modeling is an important, growing field in computational science and engineering, and this is the first book to address the subject in relation to computational fluid dynamics. It focuses on complex parametrization of shapes for their optimization and includes recent developments in advanced topics such as turbulence, stability of flows, inverse problems, optimization, and flow control, as well as applications.This book will be of interest to researchers and graduate students in the field of reduced order modeling. Reduced order modeling is an important, growing field in computational science and engineering, and this is the first book to address the subject in relation to computational fluid dynamics. It focuses on complex parametrization of shapes for their optimization and includes recent developments in advanced topics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781611977240
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ISBN 13: 9781611977240
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ISBN 13: 9781611977240
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ISBN 13: 9781611977240
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Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics
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ISBN 13: 9781611977240
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Paperback. Condición: New. Reduced order modeling is an important, growing field in computational science and engineering, and this is the first book to address the subject in relation to computational fluid dynamics. It focuses on complex parametrization of shapes for their optimization and includes recent developments in advanced topics such as turbulence, stability of flows, inverse problems, optimization, and flow control, as well as applications.This book will be of interest to researchers and graduate students in the field of reduced order modeling. Nº de ref. del artículo: LU-9781611977240
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