关键词: 流固耦合, 血管壁, 数据驱动, 周期性校正, 平滑粒子流体动力学（SPH）

Abstract: Aiming at the problem of making well balance between computational efficiency and visual realism in the traditional coupling of blood flow and vessel wall, a data driven simulation method based on Periodic-corrected Network（PcNet） for coupling of blood flow and vessel wall is proposed. The feature vector of blood flow particle state based on Smooth Particle Hydrodynamics（SPH） is designed to model the mixed contributions of neighboring proxy particles on the blood vessel wall and neighboring blood particles. A semi-supervised neural network, also called improved periodic-corrected neural network is proposed. It predicts the acceleration of each particle in the next frame. The experimental results show that the simulation method achieves fast, stable and realistic coupling result of blood flow and vessel wall.

Key words: fluid-solid coupling, blood vessel, data-driven, periodic-corrected, Smooth Particle Hydrodynamics（SPH）