69 citations · 109 across the 7 of their papers we have counts for
7 papers
Deformable registration and generative modelling of aortic anatomies by auto-decoders and neural ODEs
Riccardo Tenderini, Luca Pegolotti, Fanwei Kong +4
This work introduces AD-SVFD, a deep learning model for the deformable registration of vascular shapes to a pre-defined reference and for the generation of synthetic anatomies. AD-…
Bayesian Windkessel calibration using optimized 0D surrogate models
Jakob Richter, Jonas Nitzler, Luca Pegolotti +6
Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibrati…
Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences
Natalia L. Rubio, Luca Pegolotti, Martin R. Pfaller +2
Reduced-order models (ROMs) allow for the simulation of blood flow in patient-specific vasculatures without the high computational cost and wait time associated with traditional co…
Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks
Luca Pegolotti, Martin R. Pfaller, Natalia L. Rubio +4
Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience reduced accuracy when working with anatomies…
Model order reduction of flow based on a modular geometrical approximation of blood vessels
Luca Pegolotti, Martin Pfaller, Alison Marsden +1
We are interested in a reduced order method for the efficient simulation of blood flow in arteries. The blood dynamics is modeled by means of the incompressible Navier-Stokes equat…
Data driven approximation of parametrized PDEs by Reduced Basis and Neural Networks
Niccolò Dal Santo, Simone Deparis, Luca Pegolotti
We are interested in the approximation of partial differential equations with a data-driven approach based on the reduced basis method and machine learning. We suppose that the phe…
Coupling non-conforming discretizations of PDEs by spectral approximation of the Lagrange multiplier space
Simone Deparis, Luca Pegolotti
This work focuses on the development of a non-conforming domain decomposition method for the approximation of PDEs based on weakly imposed transmission conditions: the continuity o…