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In den WarenkorbZustand: New. Redaktion: Karthik DuraisamyKarthik Duraisamy is a professor of Aerospace Engineering and the director of the Michigan Institute for Computational Discovery at the University of Michigan, Ann Arbor, USA. His research interests are in data-driven .
Sprache: Englisch
Verlag: Elsevier Science & Technology Jun 2025, 2025
ISBN 10: 0323950434 ISBN 13: 9780323950435
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Taschenbuch. Zustand: Neu. Neuware - Data-driven Analysis and Modeling of Turbulent Flows provides an integrated treatment of modern data-driven methods to describe, control, and predict turbulent flows through the lens of both physics and data science.The book is organized into three parts:¿ Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods¿ Methods for estimation and control using data assimilation and machine learning approaches¿ Finally, novel modeling techniques that combine physical insights with machine learningThis book is intended for students, researchers, and practitioners in fluid mechanics, though readers from related fields such as applied mathematics, computational science, and machine learning will find it also of interest.
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In den WarenkorbPaperback. Zustand: Brand New. 460 pages. 9.00x6.00x9.00 inches. In Stock. This item is printed on demand.
Sprache: Englisch
Verlag: Elsevier Science & Technology, Oxford, 2025
ISBN 10: 0323950434 ISBN 13: 9780323950435
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In den WarenkorbPaperback. Zustand: new. Paperback. Data-driven Analysis and Modeling of Turbulent Flows provides an integrated treatment of modern data-driven methods to describe, control, and predict turbulent flows through the lens of both physics and data science.The book is organized into three parts: Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods Methods for estimation and control using data assimilation and machine learning approaches Finally, novel modeling techniques that combine physical insights with machine learningThis book is intended for students, researchers, and practitioners in fluid mechanics, though readers from related fields such as applied mathematics, computational science, and machine learning will find it also of interest. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.