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.

翻译：在这项工作中,开发了一个基于机械学习的减序模型(ROM),以快速和可靠的方式调查在科诺氏动脉旁截肢(CABG)特定配置的病人配置中血液动力学模式。计算域由冠状动脉左侧的左分支组成,当左主冠状动脉发生加速硬化时。通过使用适当的正正向分辨算法(POD)从不可压缩导航-斯托克斯方程式的卷(FV)溶液中抽取了一个缩小的基础空间。人工神经网络(ANNS)被用于计算模型系数。通过非不成正态逻辑基底量参数化来改变体积模变形(FFD),从而引入了加速度。