Lagrangian Particle

Lagrangian particle methods track the movement of individual particles to model fluid dynamics and other physical processes, offering advantages over grid-based approaches, especially for systems with complex geometries or free surfaces. Current research focuses on improving the accuracy and efficiency of these methods, particularly through the development of machine learning models like graph neural networks and deep learning-based super-resolution techniques to enhance data quality and reduce computational cost. These advancements are impacting diverse fields, including system identification from video data, atmospheric modeling, and the emulation of computationally expensive simulations, leading to more accurate and efficient scientific computations.