A non-intrusive data-driven ROM framework for hemodynamics problems

Abstract

Reduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PDE) by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem. In this contribution, we propose an efficient non-intrusive data-driven framework involving ROM techniques in computational fluid dynamics (CFD) for hemodynamics applications. By starting from a database of high-fidelity solutions related to a certain values of the parameters, we apply the proper orthogonal decomposition with interpolation (PODI) and then reconstruct the variables of interest for new values of the parameters, i.e. different values from the ones included in the database. Furthermore, we present a preliminary web application through which one can run the ROM with a very user-friendly approach, without the need of having expertise in the numerical analysis and scientific computing field. The case study we have chosen to test the efficiency of our algorithm is represented by the aortic blood flow pattern in presence of a Left Ventricular Assist Device (LVAD) when varying the pump flow rate.

arXiv commentarXiv admin note: substantial text overlap with arXiv:2007.03527

References in corpus (5)

• A POD-Galerkin reduced order model for a LES filtering approach
• A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces
• Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods
• Reduced order methods for parametric optimal flow control in coronary bypass grafts, towards patient-specific data assimilation
• Non-intrusive PODI-ROM for patient-specific aortic blood flow in presence of a LVAD device

Cited by in corpus (5)

• Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
• Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters
• Non-intrusive PODI-ROM for patient-specific aortic blood flow in presence of a LVAD device
• Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
• Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation