Artificial Intelligence for Computational Fluid Dynamics

Kamarul Arifin Ahmad, Mohammad Jawaid, Balbir Singh

• Publisher: Academic Press
• Year: 2026
• Topic: Technology & Engineering
• eBook ISBN:
  9780443291197
• Subtopic: Fluid Mechanics
• Index: Technology & Engineering

Artificial Intelligence for Computational Fluid Dynamics offers a groundbreaking exploration of how high-performance computing and artificial intelligence (AI) revolutionize computational fluid dynamics (CFD). Designed for students and researchers alike, this book consolidates information on machine learning, deep learning, and neural networking, demonstrating their transformative potential in CFD. With an emphasis on both the current state of the field and future possibilities, the text serves as a vital resource for anyone looking to understand and leverage AI in fluid dynamics. This work not only educates but also inspires innovation within a rapidly evolving scientific landscape.The book delves into future research directions, aligning with the newly amended CFD vision for 2030, and underscores the importance of continued development. It introduces scientific tools and software integral to AI-driven CFD applications, ensuring that readers gain practical knowledge alongside theoretical insights. Authored by renowned experts with extensive teaching and research experience, this work stands at the intersection of cutting-edge technology and academic rigor, making it an indispensable reference for anyone in the field. - Encompasses a wide range of examples, pictures, and experimental works from historical and contemporary AI research, incorporating computational fluid dynamics (CFD) in almost all its forms - Serves as a comprehensive guide that explores the utilization of modern AI techniques and fluid dynamics computing methods, offering practical applications for researchers and students, both present and future - Includes updates on the emerging field of quantum computing and its implications in the context of ongoing research, presenting its potential to replace high-performance computing (HPC) and its overall impact - Fills a unique gap in the market as there has never been a publication specifically dedicated to the fusion of AI and computational fluid dynamics

Table of contents

• Title of Book
• Chapter 1 Artificial intelligence and computational fluid dynamics: background
• Chapter 2 An overview of artificial intelligence and its subsets
• Chapter 3 Computational fluid dynamics approaches leveraging artificial intelligence
• Chapter 4 Enhanced reduced-order modeling and accelerated direct numerical simulation
• Chapter 5 Machine learning and deep learning architectures for computational fluid dynamics
• Chapter 6 Turbulence closure modeling using deep learning
• Chapter 7 Artificial intelligence accelerated computational fluid dynamics and neural networks
• Chapter 8 Artificial neural network as popular artificial intelligence tool for computational fluid dynamics
• Chapter 9 Support vector machine, an important supervised learning category
• Chapter 10 State of current artificial intelligence algorithms in computational fluid dynamics and their implementation
• Chapter 11 Machine learning-based optimal mesh generation and optimization
• Chapter 12 Machine learning-based new sparse algorithms and reinforcement learning for computational fluid dynamics
• Chapter 13 Modern tools, languages, and systems available for implementing artificial intelligence algorithms
• Chapter 14 Applications of artificial intelligence in computational aerodynamics and modeling
• Chapter 15 Applications of artificial intelligence in turbulence modeling
• Chapter 16 Applications of artificial intelligence for boundary layer and multiphase flows
• Chapter 17 Applications of artificial intelligence in heat and mass transfer-based flow computations and materials processing
• Chapter 18 Operating challenges for artificial intelligence in computational fluid dynamics and the available solutions
• Chapter 19 Importance of artificial intelligence in Computational Fluid Dynamics Vision 2030—editor’s perspective
• Chapter 20 Concluding remarks
• Index