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Data Driven Analysis and Modeling of Turbulent Flows by Karthik Duraisamy (Director, Center for Data-driven Computational Physics and the Air Force Center for Rocket Combustor Dynamics, University of Michigan, USA) at Abbey's Bookshop,
Data Driven Analysis and Modeling of Turbulent Flows
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Karthik Duraisamy (Director, Center for Data-driven Computational Physics and the Air Force Center for Rocket Combustor Dynamics, University of Michigan, USA)

Data Driven Analysis and Modeling of Turbulent Flows

Karthik Duraisamy (Director, Center for Data-driven Computational Physics and the Air Force Center for Rocket Combustor Dynamics, University of Michigan, USA)

$387.95

Paperback

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English
Academic Press Inc
24 April 2025
Thermodynamics & heat
Series: Computation and Analysis of Turbulent Flows
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.
Edited by:  
Imprint:   Academic Press Inc
Country of Publication:   United Kingdom
Dimensions:   Height: 229mm,  Width: 152mm, 
ISBN:   9780323950435
ISBN 10:   0323950434
Series:   Computation and Analysis of Turbulent Flows
Pages:   414
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

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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