Data Driven Analysis and Modeling of Turbulent Flows

Häftad, Engelska, 2025

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Beskrivning

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.

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

Produktinformation

Utgivningsdatum:2025-06-06
Mått:152 x 229 x 23 mm
Vikt:670 g
Format:Häftad
Språk:Engelska
Serie:Computation and Analysis of Turbulent Flows
Antal sidor:414
Förlag:Elsevier Science
ISBN:9780323950435

Utforska kategorier

Fysik inom Naturvetenskap och teknik

Mer om författaren

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.

Innehållsförteckning

1. Introduction to data-driven modeling2. Modal Decomposition3. Resolvent analysis for turbulent flows4. Data assimilation and flow estimation5. Data-driven control6. Constitutive Modeling7. Parameter estimation and uncertainty quantification8. Machine Learning Augmented modeling9. Symbolic regression methods