Over the years, computational fluid dynamics (CFD) has undergone significant evolution, establishing it as an essential tool for simulating complex fluid flow phenomena. The integration of deep learning (DL) and machine learning (ML) methodologies with CFD has recently emerged as a promising approach to enhance the accuracy, efficiency, and automation of simulations. This comprehensive review article explores the potential of ML and DL in advancing CFD, providing valuable insights into their transformative impact on the field. In this study, the large eddy simulation (LES), reduced-order models (ROMs), Reynolds-averaged Navier-Stokes (RANS), and direct numerical simulation (DNS) techniques, along with the role of ML in improving these models within CFD, are thoroughly discussed. It was demonstrated that ML and DL techniques can accelerate high-fidelity simulations, provide turbulence models with varying levels of precision, and generate ROMs that surpass the accuracy achieved through conventional methods. Additionally, this paper presents a broad perspective, highlighting recent advancements, opportunities, and unresolved challenges in the field.
Omrani et al. (Thu,) studied this question.