Artificial Intelligence for Computational Fluid Dynamics

Name: Artificial Intelligence for Computational Fluid Dynamics
Price: 260 USD
Availability: InStock
ISBN: 9780443291180

by Kamarul Arifin Ahmad, Mohammad Jawaid, Balbir Singh

1st Edition

Publisher: Academic Press

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Mechanical Engineering

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Book Details

Print ISBN	9780443291180
eText ISBN	9780443291197
Publisher	Academic Press
Publishing Year	2026
Edition	1st Edition
Language	English
Pages	584

Artificial Intelligence for Computational Fluid Dynamics, 1st Edition, examines how artificial intelligence and high-performance computing intersect with numerical fluid simulations. Edited by Kamarul Arifin Ahmad, Mohammad Jawaid, and Balbir Singh, this volume addresses advanced academics seeking to integrate machine learning techniques into computational fluid dynamics research.

The text organizes core concepts across machine learning architectures, artificial neural networks, deep learning models, and support vector machines. It addresses fluid flow optimization, dynamic mode decomposition, sparse algorithms, and reduced order modeling alongside direct numerical simulation. Coverage extends to specialized physical domains, including aerodynamic modeling, heat and mass transfer, and multiphase boundary layer flows.

A dedicated focus connects current algorithmic implementations with the long-term goals of the amended CFD Vision 2030. Designed for graduate students, post-doctoral scholars, faculty, and research staff, this work published by Academic Press provides a structural reference for modern computational fluid mechanics research.

Chapter 1: Artificial Intelligence and Computational Fluid Dynamics: Background
Chapter 2: Introduction to artificial intelligence and subsets
Chapter 3: Artificial intelligence based computational fluid dynamics approaches
Chapter 4: Enhanced reduced order modeling and accelerated direct numerical simulation
Chapter 5: Machine learning/ Deep Learning architectures and Computational Fluid Dynamics
Chapter 6: Turbulence Closure Modeling using Deep Learning
Chapter 7: DNNs – CNNs/RNNs/PINNs/cPINNs/xPINNs
Chapter 8: ANN as popular AI tool for CFD
Chapter 9: Support Vector Machine (SVM) an important Supervised Learning Category
Chapter 10: Current AI algorithms in CFD and implementation
Chapter 11: AI for accelerated CFD and fluid flow optimization
Chapter 12: Dynamic Model Decomposition of complex Fluid Flow Analysis using Machine Learning
Chapter 13: Machine learning based optimal mesh generation and optimization
Chapter 14: Machine learning based New sparse algorithms
Chapter 15: Commercial and open source models/codes used in industry for AI and CFD
Chapter 16: Modern tools, languages and systems available for implementing AI algorithms.
Chapter 17: Aerodynamic Modeling in CFD using AI
Chapter 18: Application of AI for Turbulence Modeling
Chapter 19: Application of AI in CFD for Boundary layer and Multiphase Flows
Chapter 20: AI in Heat and Mass Transfer using CFD
Chapter 21: AI for CFD in materials industry and other applications
Chapter 22: Operating challenges for AI in CFD and the available solutions
Chapter 23: AI, CFD and CFD Vision 2030
Chapter 23: Conclusion Remarks