Periodic-corrected data-driven coupling of blood flow and the vessel wall for virtual surgery

Fast and realistic coupling of blood flow and the vessel wall is of great importance to virtual surgery. In this paper, we propose a novel data-driven coupling method that formulates physics-based blood flow simulation as a regression problem, using an improved periodic-corrected neural network, estimating the acceleration of every particle at each frame to obtain fast, stable, and realistic simulation. We design a particle state feature vector based on smoothed particle hydrodynamics, modeling the mixed contribution of neighboring proxy particles on the blood vessel wall and neighboring blood particles, giving the extrapolation ability to deal with more complex couplings. We present a semi-supervised training strategy to improve the traditional back propagation neural network, which corrects the error periodically to ensure long-term stability. Experimental results demonstrate that our method is able to implement stable and vivid coupling of blood flow and the vessel wall while greatly improving computational efficiency.