  Fast and realistic coupling of blood flow and 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
(PcNet), 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 (SPH), 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 BP 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 vessel wall while
greatly improving computational efficiency.
