Michele Girfoglio
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Michele Girfoglio has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Gianluigi Rozza
Mathematics Area, mathLab, SISSA, Trieste, Italy
4 shared publications - Annalisa Quaini
Department of Mathematics, University of Houston, 3551 Cullen Blvd, Houston TX 77204, USA
2 shared publications - Francesco Ballarin
Department of Mathematics and Physics, Università Cattolica del Sacro Cuore, Brescia, Italy
2 shared publications - Federico Pichi
SISSA
1 shared publication - Pasquale Claudio Africa
MathLab, Mathematics Area, SISSA Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, Trieste, 34136, Italy
1 shared publication - Pierfrancesco Siena
Mathematics Area MathLab SISSA International School for Advanced Studies Via Bonomea 265 34136 Trieste Italy
1 shared publication
Latest Publications
- 24 May 2025
- Published by Elsevier in Computer Methods and Programs in Biomedicine
- Vol. 269, 108813
Projection-based reduced order modelling for unsteady parametrized optimal control problems in 3D cardiovascular flows
- 24 May 2025
- Published by Elsevier in Computer Methods and Programs in Biomedicine
- Vol. 269, 108813
Projection-based reduced order modelling for unsteady parametrized optimal control problems in 3D cardiovascular flows
- 1 January 2023
- Published by MathDoc/Centre Mersenne in Comptes Rendus Mécanique
- Vol. 351 (S1) , 457-477
A linear filter regularization for POD-based reduced-order models of the quasi-geostrophic equations
We propose a regularization for reduced-order models (ROMs) of the quasi-geostrophic equations (QGE) to increase accuracy when the proper orthogonal decomposition (POD) modes retained to construct the reduced basis are insufficient to describe the system dynamics. Our regularization is based on the so-called BV- model, which modifies the nonlinear term in the QGE and adds a linear differential filter for the vorticity. To show the effectiveness of the BV- model for ROM closure, we compare the results computed by a POD-Galerkin ROM with and without regularization for the classical double-gyre wind forcing benchmark. Our numerical results show that the solution computed by the regularized ROM is more accurate, even when the retained POD modes account for a small percentage of the eigenvalue energy. Additionally, we show that, although computationally more expensive than the ROM with no regularization, the regularized ROM is still a competitive alternative to full-order simulations of the QGE.- 1 January 2023
- Published by MathDoc/Centre Mersenne in Comptes Rendus Mécanique
- Vol. 351 (S1) , 457-477
A linear filter regularization for POD-based reduced-order models of the quasi-geostrophic equations
We propose a regularization for reduced-order models (ROMs) of the quasi-geostrophic equations (QGE) to increase accuracy when the proper orthogonal decomposition (POD) modes retained to construct the reduced basis are insufficient to describe the system dynamics. Our regularization is based on the so-called BV- model, which modifies the nonlinear term in the QGE and adds a linear differential filter for the vorticity. To show the effectiveness of the BV- model for ROM closure, we compare the results computed by a POD-Galerkin ROM with and without regularization for the classical double-gyre wind forcing benchmark. Our numerical results show that the solution computed by the regularized ROM is more accurate, even when the retained POD modes account for a small percentage of the eigenvalue energy. Additionally, we show that, although computationally more expensive than the ROM with no regularization, the regularized ROM is still a competitive alternative to full-order simulations of the QGE.- 4 August 2022
- Published by Elsevier in Journal of Computational and Applied Mathematics
- Vol. 418, 114656
A novel Large Eddy Simulation model for the Quasi-Geostrophic equations in a Finite Volume setting
- 4 August 2022
- Published by Elsevier in Journal of Computational and Applied Mathematics
- Vol. 418, 114656
A novel Large Eddy Simulation model for the Quasi-Geostrophic equations in a Finite Volume setting
- Open Access
Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters
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.
Open AccessData-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters
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.