60 citations · 308 across the 42 of their papers we have counts for
7 papers · 1 filter
A linear filter regularization for POD-based reduced order models of the quasi-geostrophic equations
Michele Girfoglio, Annalisa Quaini, Gianluigi Rozza
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 retai…
A data-driven Reduced Order Method for parametric optimal blood flow control: application to coronary bypass graft
Caterina Balzotti, Pierfrancesco Siena, Michele Girfoglio +2
We consider an optimal flow control problem in a patient-specific coronary artery bypass graft with the aim of matching the blood flow velocity with given measurements as the Reyno…
Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters
Pierfrancesco Siena, Michele Girfoglio, Francesco Ballarin +1
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 Bypa…
An introduction to POD-Greedy-Galerkin reduced basis method
Pierfrancesco Siena, Michele Girfoglio, Gianluigi Rozza
Partial differential equations can be used to model many problems in several fields of application including, e.g., fluid mechanics, heat and mass transfer, and electromagnetism. A…
A novel Large Eddy Simulation model for the Quasi-Geostrophic Equations in a Finite Volume setting
Michele Girfoglio, Annalisa Quaini, Gianluigi Rozza
We present a Large Eddy Simulation (LES) approach based on a nonlinear differential low-pass filter for the simulation of two-dimensional barotropic flows with under-refined meshes…
A POD-Galerkin reduced order model for the Navier-Stokes equations in stream function-vorticity formulation
Michele Girfoglio, Annalisa Quaini, Gianluigi Rozza
We develop a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for the efficient numerical simulation of the parametric Navier-Stokes equations in the…
Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural network
Pierfrancesco Siena, Michele Girfoglio, Gianluigi Rozza
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…