There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Mathematical and numerical modelling of the cardiovascular system is a research topic that
has attracted remarkable interest from the mathematical community because of its intrinsic …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Conventional reduced order modeling techniques such as the reduced basis (RB) method
(relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …

This work proposes an approach that combines a library of component‐based reduced‐
order models with Bayesian state estimation in order to create data‐driven physics‐based …

Alfio Quarteroni Third Edition Page 1 MS&A – Modeling, Simulation and Applications 16
Numerical Models for Di erential Problems Alfio Quarteroni Third Edition Page 2 MS&A Volume …

We propose a data-driven Model Order Reduction (MOR) technique, based on Artificial
Neural Networks (ANNs), applicable to dynamical systems arising from Ordinary Differential …

The polynomial chaos (PC) expansion has been widely used as a surrogate model in the
Bayesian inference to speed up the Markov chain Monte Carlo (MCMC) calculations …

We propose a dimension reduction technique for Bayesian inverse problems with nonlinear
forward operators, non-Gaussian priors, and non-Gaussian observation noise. The …

We explore using neural operators, or neural network representations of nonlinear maps
between function spaces, to accelerate infinite-dimensional Bayesian inverse problems …