2 citations · 2 across the 4 of their papers we have counts for
8 papers
Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
Eugenio Varetti, Matteo Torzoni, Marco Tezzele +1
This work shows how adaptivity can enhance value realization of digital twins in civil engineering. We focus on adapting the state transition models within digital twins represente…
Multi-Agent Digital Twins for Strategic Decision-Making using Active Inference
Francesco Maria Mancinelli, Matteo Torzoni, Domenico Maisto +4
Active Inference is an emerging framework providing a quantitative account of behavioral processes in neuroscience and a principled approach to decision-making under uncertainty. I…
Active Digital Twins via Active Inference
Matteo Torzoni, Domenico Maisto, Andrea Manzoni +3
Digital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes. However, co…
Neural Markov chain Monte Carlo: Bayesian inversion via normalizing flows and variational autoencoders
Giacomo Bottacini, Matteo Torzoni, Andrea Manzoni
This paper introduces a Bayesian framework that combines Markov chain Monte Carlo (MCMC) sampling, dimensionality reduction, and neural density estimation to efficiently handle inv…
Progressive multi-fidelity learning with neural networks for physical system predictions
Paolo Conti, Mengwu Guo, Attilio Frangi +1
Highly accurate datasets from numerical or physical experiments are often expensive and time-consuming to acquire, posing a significant challenge for applications that require prec…
Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
Filippo Zacchei, Paolo Conti, Attilio Alberto Frangi +1
Inverse uncertainty quantification (UQ) tasks such as parameter estimation are computationally demanding whenever dealing with physics-based models, and typically require repeated…