  In this work the development of a machine learning-based Reduced Order Model
(ROM) for the investigation of hemodynamics in a patient-specific configuration
of Coronary Artery Bypass Graft (CABG) is proposed. The computational domain is
referred to left branches of coronary arteries when a stenosis of the Left Main
Coronary Artery (LMCA) occurs. The method extracts a reduced basis space from a
collection of high-fidelity solutions via a Proper Orthogonal Decomposition
(POD) algorithm and employs Artificial Neural Networks (ANNs) for the
computation of the modal coefficients. The Full Order Model (FOM) is
represented by the incompressible Navier-Stokes equations discretized using a
Finite Volume (FV) technique. Both physical and geometrical parametrization are
taken into account, the former one related to the inlet flow rate and the
latter one related to the stenosis severity. With respect to the previous works
focused on the development of a ROM framework for the evaluation of coronary
artery disease, the novelties of our study include the use of the FV method in
a patient-specific configuration, the use of a data-driven ROM technique and
the mesh deformation strategy based on a Free Form Deformation (FFD) technique.
The performance of our ROM approach is analyzed in terms of the error between
full order and reduced order solutions as well as the speedup achieved at the
online stage.
