Fast Simulation of Particulate Suspensions Enabled by Graph Neural Network Part I: Theory and Framework

Predicting the dynamic behaviors of particles in suspension subject to hydrodynamic interaction (HI) and external drive can be critical for many applications. By harvesting advanced deep learning techniques, we present a new framework, hydrodynamic interaction graph neural network (HIGNN), for inferring and predicting the particles' dynamics in Stokes suspensions. It overcomes the limitations of traditional approaches in computational efficiency, accuracy, and/or transferability. In particular, by uniting the data structure represented by a graph and the neural networks with learnable parameters, the HIGNN constructs surrogate modeling for the mobility tensor of particles which is the key to predicting the dynamics of particles subject to HI and external forces. It can accurately capture both the long-range HI and short-range lubrication effects. In this talk, we introduce the HIGNN framework and demonstrate its accuracy, efficiency, and transferability in a variety of particulate suspension systems.