activity
20152026
most citedModeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations

128 citations · 990 across the 51 of their papers we have counts for

collaborators
Showing 2023Show all

11 papers · 1 filter

math.NA2023

Space--time Isogeometric Analysis of cardiac electrophysiology

P. F. Antonietti, L. Dedè, G. Loli +3

This work proposes a stabilized space--time method for the monodomain equation coupled with the Rogers--McCulloch ionic model, which is widely used to simulate electrophysiological…

math.NA2023

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-…

math.NA2023★ 3 cited

Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Matteo Salvador, Marina Strocchi, Francesco Regazzoni +3

Cardiac digital twins provide a physics and physiology informed framework to deliver predictive and personalized medicine. However, high-fidelity multi-scale cardiac models remain…

math.NA2023

Preserving the positivity of the deformation gradient determinant in intergrid interpolation by combining RBFs and SVD: application to cardiac electromechanics

Michele Bucelli, Francesco Regazzoni, Luca Dede' +1

The accurate robust and efficient transfer of the deformation gradient tensor between meshes of different resolution is crucial in cardiac electromechanics simulations. We present…

math.NA2023★ 9 cited

A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system

Andrea Tonini, Christian Vergara, Francesco Regazzoni +4

Impaired cardiac function has been described as a frequent complication of COVID-19-related pneumonia. To investigate possible underlying mechanisms, we represented the cardiovascu…

cs.LG2023★ 5 cited

Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes

Francesco Regazzoni, Stefano Pagani, Matteo Salvador +2

Predicting the evolution of systems that exhibit spatio-temporal dynamics in response to external stimuli is a key enabling technology fostering scientific innovation. Traditional…