Recent advances in machine learning applications to computational fluid dynamics (CFD): models, challenges and future perspectives

Abstract

Integrating machine learning (ML) techniques into computational fluid dynamics (CFD) has emerged as a promising model to speed up simulations, enhance turbulence modelling, improve prediction accuracies, and enable real-time flow analysis Comparative performance is also examined Critical analysis of previous work reveals current challenges, including limited generalization in flow regimes, high data requirements, and lack of robust uncertainty quantification The paper also outlines future approaches, including hybrid physics data-driven frameworks, transfer learning, interpretability, and open collaborative platforms Emphasis is By synthesizing developments and identifying research gaps, this study provides insights that can guide the development of ML–CFD towards robust, scalable, and industry-ready solutions

Keywords:- Machine Learning, Computational Fluid Dynamics, Turbulence Modelling, Hybrid Approaches, Uncertainty Quantification