Artificial Intelligence in Fluid Mechanics
Traditionally, the underlying physics of fluid mechanics has been explored by theoretical and computational methods along with experimental measurements. Recently, there has been a resurgence of data-driven and machine learning methods to provide a fourth pillar as a unifying force towards improved understanding and controlling of fluid flow. The focus of this special issue is on the symbiosis of traditional modeling with the data-driven methods for solving fluid problems, e.g., integration of AI/ML techniques with computational and experimental fluid dynamics. The contributions below include recent advances in data-driven techniques for fluid mechanics, and showcase the application of these methods in applied science and engineering.
Guest editors: Rajeev K. Jaiman, Weiwei Zhang, and Dixia Fan