5 citations · 10 across the 8 of their papers we have counts for
8 papers
Full-field surrogate modeling of cardiac function encoding geometric variability
Elena Martinez, Beatrice Moscoloni, Matteo Salvador +3
Combining physics-based modeling with data-driven methods is critical to enabling the translation of computational methods to clinical use in cardiology. The use of rigorous differ…
Liquid Fourier Latent Dynamics Networks for fast GPU-based numerical simulations in computational cardiology
Matteo Salvador, Alison L. Marsden
Scientific Machine Learning (ML) is gaining momentum as a cost-effective alternative to physics-based numerical solvers in many engineering applications. In fact, scientific ML is…
An integrated heart-torso electromechanical model for the simulation of electrophysiogical outputs accounting for myocardial deformation
Elena Zappon, Matteo Salvador, Roberto Piersanti +3
When generating in-silico clinical electrophysiological outputs, such as electrocardiograms (ECGs) and body surface potential maps (BSPMs), mathematical models have relied on singl…
A Modular Framework for Implicit 3D-0D Coupling in Cardiac Mechanics
Aaron L. Brown, Matteo Salvador, Lei Shi +6
In numerical simulations of cardiac mechanics, coupling the heart to a model of the circulatory system is essential for capturing physiological cardiac behavior. A popular and effi…
lifex-ep: a robust and efficient software for cardiac electrophysiology simulations
Pasquale C. Africa, Roberto Piersanti, Francesco Regazzoni +6
Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-…
Branched Latent Neural Maps
Matteo Salvador, Alison Lesley Marsden
We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input-output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedfo…