2 citations · 2 across the 4 of their papers we have counts for
5 papers
Accelerated Patient-Specific Hemodynamic Simulations with Hybrid Physics-Based Neural Surrogates
Natalia L. Rubio, Eric F. Darve, Alison L. Marsden
Physics-based 0D reduced-order models provide computationally lightweight predictions of cardiovascular flows, resolving bulk hemodynamics in fractions of a second that would take…
Data-Driven Bifurcation Handling in Physics-Based Reduced-Order Vascular Hemodynamic Models
Natalia L. Rubio, Eric F. Darve, Alison L. Marsden
Three-dimensional (3D) finite-element simulations of cardiovascular flows provide high-fidelity predictions to support cardiovascular medicine, but their high computational cost li…
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…
On the fractional Laplacian of variable order
Eric Darve, Marta D'Elia, Roberto Garrappa +2
We present a novel definition of variable-order fractional Laplacian on Rn based on a natural generalization of the standard Riesz potential. Our definition holds for values of the…