  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. In this Letter, we propose a generic
machine-learning-augmented reduced model for these flows. Our model replaces
expensive parts of a numerical scheme with multilayer perceptrons. Given the
physical parameters of the particle, our model generalizes to arbitrary
geometries and boundary conditions without the need to retrain the regression
function. It is 10 times faster than a state-of-the-art numerical scheme having
the same number of degrees of freedom and can reproduce several features of the
flow quite accurately. We illustrate the performance of our model on integral
equation formulation of vesicle suspensions in two dimensions.
