Droplet Dynamic

Droplet dynamics research focuses on understanding and predicting the behavior of liquid droplets, encompassing their formation, motion, interaction, and breakup. Current investigations leverage machine learning, particularly neural networks (including convolutional, recurrent, and physics-informed neural networks), to model complex droplet behaviors, accelerate simulations, and analyze experimental data such as wide-angle light scattering images. These advancements enable more efficient analysis of droplet size distributions in applications ranging from inkjet printing and spray synthesis to cloud modeling and material science, improving our understanding of diverse physical processes. The resulting insights are crucial for optimizing industrial processes and advancing our knowledge of natural phenomena.