6 papers
A Neural Latent Dynamics Approach for Solving Inverse Problems in Cardiac Electrophysiology
Edoardo Centofanti, Giovanni Ziarelli, Simone Scacchi +1
Solving inverse problems in cardiac electrophysiology consists in the recovery of physiological parameters from surface electrocardiogram (ECG) measurements, a task which is often…
Learning cardiac activation and repolarization times with operator learning
Edoardo Centofanti, Giovanni Ziarelli, Nicola Parolini +3
Solving partial or ordinary differential equation models in cardiac electrophysiology is a computationally demanding task, particularly when high-resolution meshes are required to…
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…
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…
Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control
Giovanni Ziarelli, Nicola Parolini, Marco Verani
Since infectious pathogens start spreading into a susceptible population, mathematical models can provide policy makers with reliable forecasts and scenario analyses, which can be…
Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: an application to the Italian scenario
Giovanni Ziarelli, Luca Dede', Nicola Parolini +2
In the context of SARS-CoV-2 pandemic, mathematical modelling has played a fundamental role for making forecasts, simulating scenarios and evaluating the impact of preventive polit…