Aerodynamic Force

Aerodynamic force, encompassing drag, lift, and other forces acting on objects moving through air, is a crucial area of study with objectives focused on accurate prediction and control. Current research emphasizes data-driven approaches, employing machine learning models like deep neural networks (including variations like DeepONets and convolutional neural networks), Gaussian processes, and diffusion probabilistic models to predict aerodynamic forces from geometry and flow conditions, often leveraging reduced-order models to improve computational efficiency. These advancements are significantly impacting fields like aerospace, automotive, and robotics, enabling faster design optimization, more robust control systems, and improved performance in various applications.