Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract)

摘要

This study develops a physics-informed neural network (PINN) framework to predict droplet spreading dynamics on unstructured rough surfaces. The trained model effectively captures temporal evolution of the droplet shape, contact line motion, and interfacial deformation. This integration of multiphase physics with neural networks provides a mesh-free and computationally efficient alternative to numerical solvers, enabling rapid analysis and design of wettability-controlled surfaces, microfluidic devices.