activity
20182021
most citedIntegration of activation maps of epicardial veins in computational cardiac electrophysiology

17 citations · 19 across the 4 of their papers we have counts for

collaborators

11 papers

math.NA2021

Preconditioners for robust optimal control problems under uncertainty

Fabio Nobile, Tommaso Vanzan

The discretization of robust quadratic optimal control problems under uncertainty using the finite element method and the stochastic collocation method leads to large saddle-point…

math.NA2021

Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings

Juan Pablo Madrigal-Cianci, Fabio Nobile, Raul Tempone

In this work, we present, analyze, and implement a class of Multi-Level Markov chain Monte Carlo (ML-MCMC) algorithms based on independent Metropolis-Hastings proposals for Bayesia…

math.NA202117 cited

Integration of activation maps of epicardial veins in computational cardiac electrophysiology

Simone Stella, Christian Vergara, Massimiliano Maines +7

In this work we address the issue of validating the monodomain equation used in combination with the Bueno-Orovio ionic model for the prediction of the activation times in cardiac…

math.NA2020

Regularity and sparse approximation of the recursive first moment equations for the lognormal Darcy problem

Francesca Bonizzoni, Fabio Nobile

We study the Darcy boundary value problem with log-normal permeability field. We adopt a perturbation approach, expanding the solution in Taylor series around the nominal value of…

math.NA2020

Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval

Yoshihito Kazashi, Fabio Nobile

An existence result is presented for the dynamical low rank (DLR) approximation for random semi-linear evolutionary equations. The DLR solution approximates the true solution at ea…

math.OC20192 cited

A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters

Matthieu Martin, Fabio Nobile, Panagiotis Tsilifis

In this paper, we present a multilevel Monte Carlo (MLMC) version of the Stochastic Gradient (SG) method for optimization under uncertainty, in order to tackle Optimal Control Prob…