Francesco Regazzoni
Orcid: 0000-0002-4207-1400Affiliations:
- Politecnico di Milano, MOX - Dipartimento di Matematica, Milan, Italy
According to our database1,
Francesco Regazzoni
authored at least 31 papers
between 2019 and 2024.
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Bibliography
2024
Robust Radial Basis Function Interpolation Based on Geodesic Distance for the Numerical Coupling of Multiphysics Problems.
SIAM J. Sci. Comput., 2024
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.
npj Digit. Medicine, 2024
An electromechanics-driven fluid dynamics model for the simulation of the whole human heart.
J. Comput. Phys., 2024
Elucidating the cellular determinants of the end-systolic pressure-volume relationship of the heart via computational modelling.
CoRR, 2024
A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts.
CoRR, 2024
CoRR, 2024
Two new calibration techniques of lumped-parameter mathematical models for the cardiovascular system.
CoRR, 2024
An integrated heart-torso electromechanical model for the simulation of electrophysiogical outputs accounting for myocardial deformation.
CoRR, 2024
2023
BMC Bioinform., December, 2023
Fast and robust parameter estimation with uncertainty quantification for the cardiac function.
Comput. Methods Programs Biomed., April, 2023
Reconstructing relaxed configurations in elastic bodies: Mathematical formulation and numerical methods for cardiac modeling.
CoRR, 2023
Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models.
CoRR, 2023
Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.
CoRR, 2023
Preserving the positivity of the deformation gradient determinant in intergrid interpolation by combining RBFs and SVD: application to cardiac electromechanics.
CoRR, 2023
A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system.
CoRR, 2023
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes.
CoRR, 2023
2022
A cardiac electromechanical model coupled with a lumped-parameter model for closed-loop blood circulation.
J. Comput. Phys., 2022
A comprehensive and biophysically detailed computational model of the whole human heart electromechanics.
CoRR, 2022
Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free surrogate models for problems in variable domains.
CoRR, 2022
The role of mechano-electric feedbacks and hemodynamic coupling in scar-related ventricular tachycardia.
Comput. Biol. Medicine, 2022
A Novel Human Atrial Electromechanical Cardiomyocyte Model with Mechano-Calcium Feedback Effect.
Proceedings of the Computing in Cardiology, 2022
2021
A machine learning method for real-time numerical simulations of cardiac electromechanics.
CoRR, 2021
3D-0D closed-loop model for the simulation of cardiac biventricular electromechanics.
CoRR, 2021
Accelerating the convergence to a limit cycle in 3D cardiac electromechanical simulations through a data-driven 0D emulator.
Comput. Biol. Medicine, 2021
Electro-Mechanical Coupling in Human Atrial Cardiomyocytes: Model Development and Analysis of Inotropic Interventions.
Proceedings of the Computing in Cardiology, CinC 2021, Brno, 2021
2020
PLoS Comput. Biol., 2020
A cardiac electromechanics model coupled with a lumped parameters model for closed-loop blood circulation. Part II: numerical approximation.
CoRR, 2020
A cardiac electromechanics model coupled with a lumped parameters model for closed-loop blood circulation. Part I: model derivation.
CoRR, 2020
An oscillation-free fully partitioned scheme for the numerical modeling of cardiac active mechanics.
CoRR, 2020
2019
Machine learning for fast and reliable solution of time-dependent differential equations.
J. Comput. Phys., 2019