Machine Learning Techniques for Computational Fluid Dynamics

Abstract

This article examines the integration of machine learning techniques into computational fluid dynamics (CFD) to enhance predictive accuracy and computational efficiency. Machine learning models, trained on vast datasets of fluid dynamics simulations, can offer new insights and improve the performance of traditional CFD methods. This research highlights the synergy between data-driven models and classical physics-based approaches, providing a comprehensive overview of current methodologies and future prospects. The results indicate significant potential for machine learning to revolutionize the field of CFD.