120 citations · 532 across the 34 of their papers we have counts for
37 papers
Optimal Transport Dropout for Structured Predictive Uncertainty
Giacomo Lorenzon, Francesco Regazzoni
Deterministic neural networks and neural operators provide point predictions with no intrinsic measure of reliability. Yet, predictive uncertainty may stem from irreducible outcome…
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…
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…
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…
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…
Elimination-compensation pruning for fully-connected neural networks
Enrico Ballini, Luca Muscarnera, Alessio Fumagalli +2
The unmatched ability of Deep Neural Networks in capturing complex patterns in large and noisy datasets is often associated with their large hypothesis space, and consequently to t…
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
Davide Carrara, Marc Hirschvogel, Francesca Bonizzoni +3
High-fidelity computational models of cardiac mechanics provide mechanistic insight into the heart function but are computationally prohibitive for routine clinical use. Surrogate…
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…
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…
Emergence of Structure in Ensembles of Random Neural Networks
Luca Muscarnera, Luigi Loreti, Giovanni Todeschini +2
Randomness is ubiquitous in many applications across data science and machine learning. Remarkably, systems composed of random components often display emergent global behaviors th…
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…
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…
Elucidating the cellular determinants of the end-systolic pressure-volume relationship of the heart via computational modelling
Francesco Regazzoni, Corrado Poggesi, Cecilia Ferrantini
The left ventricular end-systolic pressure-volume relationship (ESPVr) is a key indicator of cardiac contractility. Despite its established importance, several studies suggested th…
A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts
Giovanni Ziarelli, Stefano Pagani, Nicola Parolini +2
In this work, we aim to formalize a novel scientific machine learning framework to reconstruct the hidden dynamics of the transmission rate, whose inaccurate extrapolation can sign…
Shape-informed surrogate models based on signed distance function domain encoding
Linying Zhang, Stefano Pagani, Jun Zhang +1
We propose a non-intrusive method to build surrogate models that approximate the solution of parameterized partial differential equations (PDEs), capable of taking into account the…
Two new calibration techniques of lumped-parameter mathematical models for the cardiovascular system
Andrea Tonini, Francesco Regazzoni, Matteo Salvador +7
Cardiocirculatory mathematical models serve as valuable tools for investigating physiological and pathological conditions of the circulatory system. To investigate the clinical con…
Robust radial basis function interpolation based on geodesic distance for the numerical coupling of multiphysics problems
Michele Bucelli, Francesco Regazzoni, Luca Dede' +1
Multiphysics simulations frequently require transferring solution fields between subproblems with non-matching spatial discretizations, typically using interpolation techniques. St…
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…
Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models
Federica Caforio, Francesco Regazzoni, Stefano Pagani +5
The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the in…
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-…
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…
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…
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…
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…