Fluid Dynamic

Fluid dynamics research focuses on understanding and predicting the behavior of fluids, aiming to improve simulations and control of fluid flows in various applications. Current research heavily utilizes machine learning, employing neural networks (including physics-informed neural networks, transformers, and diffusion models) to create faster and more accurate simulations, particularly for complex geometries and turbulent flows. These advancements are impacting diverse fields, from robotics and autonomous vehicle navigation (leveraging hydrodynamic models) to optimizing industrial processes and enhancing the accuracy of weather and climate predictions. The development of large-scale benchmark datasets is also a key focus, enabling more robust evaluation and comparison of different modeling approaches.