Random Vortex

Random vortex methods are computational techniques used to model and simulate fluid dynamics, particularly focusing on the behavior of vortices— swirling patterns in fluids. Current research emphasizes developing machine learning-based approaches, such as neural networks integrated with vortex particle methods, to improve the accuracy and efficiency of fluid simulations and inference, even in complex scenarios with non-smooth or fractional equations. These advancements have implications for diverse fields, including underwater object detection (through improved image processing), robotics (via enhanced collision avoidance algorithms), and the broader study of fluid mechanics by enabling more accurate and efficient simulations of Navier-Stokes equations.