论文标题
通过人工神经网络研究冠状动脉旁路移植物的快速准确的数值模拟
Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural network
论文作者
论文摘要
在这项工作中，开发了基于机器学习的减少订购模型（ROM），以快速可靠的方式研究冠状动脉旁路搭配（CABG）的患者特异性构型中的血液动力学模式。当发生左主冠状动脉（LMCA）的狭窄时，计算结构域由冠状动脉的左分支组成。通过使用适当的正交分解（POD）算法的不可压缩的Navier-Stokes方程的有限体积（FV）溶液的集合提取了缩小的基础空间。人工神经网络（ANN）用于计算模态系数。狭窄是通过通过非均匀的理性基础样条（NURBS）量化参数化将体积变形（FFD）转移到自由形式变形（FFD）中引入的。
In this work a machine learning-based Reduced Order Model (ROM) is developed to investigate in a rapid and reliable way the hemodynamic patterns in a patient-specific configuration of Coronary Artery Bypass Graft (CABG). The computational domain is composed by the left branches of coronary arteries when a stenosis of the Left Main Coronary Artery (LMCA) occurs. A reduced basis space is extracted from a collection of Finite Volume (FV) solutions of the incompressible Navier-Stokes equations by using the Proper Orthogonal Decomposition (POD) algorithm. Artificial Neural Networks (ANNs) are employed to compute the modal coefficients. Stenosis is introduced by morphing the volume meshes with a Free Form Deformation (FFD) by means of a Non-Uniform Rational Basis Spline (NURBS) volumetric parameterization.
