Emerging trends in machine learning for computational fluid dynamics

Machine learning (ML) is a rapidly developing field of research that has transformed the state-of-the-art capabilities for many traditional tasks in computer science, such as image classification and captioning, natural language processing, and recommender systems. The numerous success stories of ML have led to widespread adoption in the scientific and engineering communities as well, fueled by a growing wealth of data, computing resources, and advanced optimization algorithms. This is especially true in the field of fluid mechanics, where emerging technologies complement existing computational and experimental methods, providing a unified approach to building models from data [5]. Despite these advancements, there remains a gap in understanding how ML can be best integrated with computational fluid dynamics (CFD). This paper aims to explore the synergies between ML and CFD, showcasing the potential benefits and challenges in combining these fields. ML can advance CFD in areas such as turbulence modeling, development of inflow boundary conditions, subgrid-scale models for large-eddy simulations (LES), closures for Reynolds-averaged Navier-Stokes (RANS) equations, development of reduced-order models (ROMs), and flow control [29]. Our approach is to first examine established techniques, such as proper-orthogonal decomposition (POD) and dynamic-mode decomposition (DMD), alongside deep-learning techniques with autoencoders. Next, we delve into emerging opportunities where ML and CFD can be further integrated, highlighting ongoing challenges and potential solutions. We conclude by summarizing the insights gained and potential future directions for this interdisciplinary research.