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Fast and realistic coupling of blood flow and vessel wall is of greatimportance to virtual surgery. In this paper, we propose a novel data-drivencoupling method that formulates physics-based blood flow simulation as aregression problem, using an improved periodic-corrected neural network(PcNet), estimating the acceleration of every particle at each frame to obtainfast, stable and realistic simulation. We design a particle state featurevector based on smoothed particle hydrodynamics (SPH), modeling the mixedcontribution of neighboring proxy particles on the blood vessel wall andneighboring blood particles, giving the extrapolation ability to deal with morecomplex couplings. We present a semi-supervised training strategy to improvethe traditional BP neural network, which corrects the error periodically toensure long term stability. Experimental results demonstrate that our method isable to implement stable and vivid coupling of blood flow and vessel wall whilegreatly improving computational efficiency.

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