Jesús Pineda
Date: 21 August 2023
Time: 5:30 PM PDT
Soft granular materials, comprising closely packed grains held together by a thin layer of lubricating fluid, display intricate many-body dynamics resulting in complex flows and rheological behavior, including plasticity and viscoelasticity, memory effects, and avalanches. Despite their widespread presence in nature and industrial applications, the structural mechanics and microscale dynamics of soft granular clusters still need to be better understood, especially those under strong confinement or surrounded by free interfaces. This work aims to bridge the gap in understanding the internal dynamics of finite-sized soft granular media by introducing a deep learning approach to characterize the shapes and movements of deformable grains in the material. We demonstrate the reliability and versatility of the method by studying the dynamics of soft granular clusters that self-organize under external flow in various physically relevant scenarios.