activity
20032026
most citedModeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations

128 citations · 1.1k across the 52 of their papers we have counts for

collaborators

58 papers

math.NA2026

A mathematical model for irreversible damage of the collagen scaffold in the myocardium

Irena Radišić, Francesco Regazzoni, Luca Dede' +1

We propose a dissipative, irreversible damage model for anisotropic media in large deformations to address the damage process of the myocardium following a cardiac infarction. We m…

math.NA2025

Cardiocirculatory Computational Models for the Study of Hypertension

Simone Celora, Andrea Tonini, Francesco Regazzoni +3

In this work, we develop patient-specific cardiocirculatory models with the aim of building Digital Twins for hypertension. In particular, in our pathophysiology-based framework, w…

math.NA2025

Improvements on uncertainty quantification with variational autoencoders

Andrea Tonini, Tan Bui-Thanh, Francesco Regazzoni +2

Inverse problems aim to determine model parameters of a mathematical problem from given observational data. Neural networks can provide an efficient tool to solve these problems. I…

math.NA2025

Influence of cellular mechano-calcium feedback in numerical models of cardiac electromechanics

Irena Radišić, Francesco Regazzoni, Michele Bucelli +3

Multiphysics and multiscale mathematical models enable the non-invasive study of cardiac function. These models often rely on simplifying assumptions that neglect certain biophysic…

q-bio.PE2025

SEIHRDV: a multi-age multi-group epidemiological model and its validation on the COVID-19 epidemics in Italy

Luca Dede', Nicola Parolini, Alfio Quarteroni +2

We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of the COVID-19 epidemic, which we validate using data from Italy starting in Septemb…

math.NA2025★ 45 cited

Combining physics-based and data-driven models: advancing the frontiers of research with Scientific Machine Learning

Alfio Quarteroni, Paola Gervasio, Francesco Regazzoni

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problem…