Machine learning acceleration of simulations of Stokesian suspensions

Particulate Stokesian flows describe the hydrodynamics of rigid or deformable particles in Stokes flows. Due to highly nonlinear fluid-structure interaction dynamics, moving interfaces, and multiple scales, numerical simulations of such flows are challenging and expensive. Here, we propose a generic machine-learning-augmented reduced model for these flows. Our model replaces expensive parts of a numerical scheme with regression functions. Given the physical parameters of the particle, our model generalizes to arbitrary geometries and boundary conditions without the need to retrain the regression functions. It is approximately an order of magnitude faster than a state-of-the-art numerical scheme using the same number of degrees of freedom and can reproduce several features of the flow accurately. We illustrate the performance of our model on integral equation formulation of vesicle suspensions in two dimensions.