activity
20202026
most citedPhysics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models

60 citations · 209 across the 29 of their papers we have counts for

collaborators
Showing math.NAShow all

23 papers · 1 filter

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.NA2026

A stability-preserving polytopal discontinuous Galerkin method for the Fisher-Kolmogorov model with applications to neurodegenerative disease modelling

Paola Francesca Antonietti, Francesca Bonizzoni, Mattia Corti +3

The Fisher--Kolmogorov equation models the spatio-temporal evolution of interacting biological species and is extensively employed in fields such as ecology, population dynamics, a…

math.NA2026

The functional impact of myofiber macroscopic organization and disarray in computational models of the murine heart

Carlo Guastamacchia, Roberto Piersanti, Francesco Giardini +5

A major challenge in computational models of cardiac electromechanics is the reconstruction of myocardial fiber architecture, as direct in vivo measurements of fiber orientation ar…

math.NA2026

Hyperelastic constitutive model discovery with differentiable finite elements and structure-preserving neural networks

Francesco Regazzoni

The discovery of constitutive laws from experimentally accessible measurements is a central problem in nonlinear computational mechanics. Many data-driven constitutive identificati…

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…