Data Driven Analysis and Modeling of Turbulent Flows (Computation and Analysis of Turbulent Flows) - Softcover
Inhaltsangabe
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 learning This 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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Über die Autorin bzw. den Autor
Karthik 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 and reduced order modeling, statistical inference, numerical methods, and Generative AI with application to fluid flows. He is also the founder and Chief Scientist of the Silicon Valley startup Geminus.AI which is focused on physics informed AI for industrial decision-making.
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Zustand: 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 . Artikel-Nr. 1966218329
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Data Driven Analysis and Modeling of Turbulent Flows
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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. Artikel-Nr. 9780323950435
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