activity
20192023
most citedPhysics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models

60 citations · 68 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2023★ 60 cited

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…

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-…

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…

math.NA2022★ 1 cited

Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free surrogate models for problems in variable domains

Francesco Regazzoni, Stefano Pagani, Alfio Quarteroni

We introduce Universal Solution Manifold Network (USM-Net), a novel surrogate model, based on Artificial Neural Networks (ANNs), which applies to differential problems whose soluti…

math.NA2021

The role of mechano-electric feedbacks and hemodynamic coupling in scar-related ventricular tachycardia

Matteo Salvador, Francesco Regazzoni, Stefano Pagani +3

Mechano-electric feedbacks (MEFs), which model how mechanical stimuli are transduced into electrical signals, have received sparse investigation by considering electromechanical si…

math.NA2019★ 2 cited

Statistical closure modeling for reduced-order models of stationary systems by the ROMES method

Stefano Pagani, Andrea Manzoni, Kevin Carlberg

This work proposes a technique for constructing a statistical closure model for reduced-order models (ROMs) applied to stationary systems modeled as parameterized systems of algebr…