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Data-Driven Reduced Order Modelling for Patient-Specific Hemodynamics of Coronary Artery Bypass Grafts with Physical and Geometrical Parameters

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Abstract

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 speed-up achieved at the online stage.

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Acknowledgements

We acknowledge the support provided by the European Research Council Executive Agency by the Consolidator Grant project AROMA-CFD “Advanced Reduced Order Methods with Applications in Computational Fluid Dynamics”-GA 681447, H2020-ERC CoG 2015 AROMA-CFD, the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Actions, grant agreement 872442 (ARIA), as well as developers and contributors of OpenFOAM®. FB also thanks the project “Reduced order modelling for numerical simulation of partial differential equations” funded by UniversitéCattolica del Sacro Cuore.

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  1. mathLab, Mathematics Area, SISSA, International School for Advanced Studies, via Bonomea 265, 34136, Trieste, Italy

    Pierfrancesco Siena, Michele Girfoglio & Gianluigi Rozza

  2. Department of Mathematics and Physics, Universitá Cattolica del Sacro Cuore, via Garzetta 48, 25133, Brescia, Italy

    Francesco Ballarin

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  1. Pierfrancesco Siena
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Siena, P., Girfoglio, M., Ballarin, F. et al. Data-Driven Reduced Order Modelling for Patient-Specific Hemodynamics of Coronary Artery Bypass Grafts with Physical and Geometrical Parameters. J Sci Comput 94, 38 (2023). https://doi.org/10.1007/s10915-022-02082-5

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