人工神经网络用于冠状动脉旁路移植术的快速准确数值模拟
Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural network
摘要：
在这项工作中，开发了一种基于机器学习的降阶模型（ROM），以快速可靠的方式研究冠状动脉旁路移植术（CABG）患者特定配置中的血流动力学模式。当左冠状动脉主干（LMCA）发生狭窄时，计算域由冠状动脉的左分支组成。使用本征正交分解（POD）算法从不可压缩Navier-斯托克斯方程的有限体积（FV）解集合中提取约化的基空间。采用人工神经网络计算模态系数。狭窄是通过非均匀有理基样条（NURBS）体积参数化的自由形式变形（FFD）变形的体积网格。
英文摘要：
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.
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