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Data Driven Analysis and Modeling of Turbulent Flows

2026-06-21 分类：Thermodynamics 评论(0)

Data Driven Analysis and Modeling of Turbulent Flows book cover

Data Driven Analysis and Modeling of Turbulent Flows

Author(s): Karthik Duraisamy

  • Publisher: Academic Press
  • Publication Date: June 20, 2025
  • Edition: 1st
  • Language: English
  • Print length: 414 pages
  • ISBN-10: 0323950434
  • ISBN-13: 9780323950435

Book Description

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

Editorial Reviews

Review

Explains methods for the analysis of large fields of data, and uncovering models and model improvements from numerical or experimental data on turbulence

From the Back Cover

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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