VesNet: Neural network accelerated solver for simulating Stokesian vesicle suspensions
For researchers simulating deformable particle suspensions, VesNet offers a practical speedup that enables larger-scale simulations on modest hardware.
VesNet accelerates 2D vesicle suspension simulations by approximating self-interactions with a neural network, achieving over 100x speedup vs. a CPU solver and 5x vs. GPU, while accurately capturing key dynamics for thousands of vesicles.
Numerical simulation of deformable particle suspensions in Stokes flow is computationally expensive due to nonlinear fluid-structure interactions, evolving interfaces, and multiscale hydrodynamics. We present VesNet, a hybrid framework that accelerates two-dimensional vesicle suspension simulations by approximating vesicle self interactions, including background flow coupling and short-range lubrication forces, while retaining conventional modules for boundary reparameterization and far-field hydrodynamics. A GPU-accelerated implementation achieves over 100x speedup compared to a multithreaded MATLAB CPU boundary integral solver and about 5x relative to its GPU counterpart. VesNet accurately captures key dynamics, including single-vesicle phase behavior, pair interactions, and large-scale suspensions in Taylor-Green and Poiseuille flows, enabling efficient simulations of thousands of vesicles on modest computational resources.