Computational Fluid Dynamic

Computational Fluid Dynamics (CFD) uses computational methods to solve fluid flow problems, aiming to predict fluid behavior and optimize designs across various applications. Current research heavily emphasizes integrating machine learning, employing architectures like Graph Neural Networks, diffusion models, and physics-informed neural networks to improve accuracy, efficiency, and scalability of CFD simulations, particularly for complex geometries and turbulent flows. This fusion of CFD and machine learning is significantly impacting scientific understanding and engineering design by accelerating simulations, enabling real-time predictions, and facilitating more complex analyses than previously possible.