Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural network (2201.01804v2)

Abstract: 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.