128 citations · 990 across the 51 of their papers we have counts for
4 papers · 1 filter
Improvements on uncertainty quantification with variational autoencoders
Andrea Tonini, Tan Bui-Thanh, Francesco Regazzoni +2
Inverse problems aim to determine model parameters of a mathematical problem from given observational data. Neural networks can provide an efficient tool to solve these problems. I…
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems
Andrea Tonini, Luca Dede'
Among other uses, neural networks are a powerful tool for solving deterministic and Bayesian inverse problems in real-time, where variational autoencoders, a specialized type of ne…
Functional-Ordinal Canonical Correlation Analysis With Application to Data from Optical Sensors
Giulia Patanè, Federica Nicolussi, Alexander Krauth +4
We address the problem of predicting a target ordinal variable based on observable features consisting of functional profiles. This problem is crucial, especially in decision-makin…
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…