Fluid Droplet

Fluid droplet research focuses on understanding droplet behavior and interactions across various scales and contexts, with primary objectives including characterizing droplet dynamics, predicting their trajectories, and quantifying their impact. Current research employs advanced computational methods, such as deep learning architectures (e.g., YOLO, DeepSORT, Physics-Informed Neural Networks) and Bayesian optimization, to analyze complex droplet phenomena, including splashing, wetting, and spreading. These advancements have significant implications for diverse fields, ranging from improving mask efficacy and inkjet printing precision to understanding fundamental fluid mechanics and developing data-driven models for complex systems.