6 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…
Enhancing Bayesian model updating in structural health monitoring via learnable mappings
Matteo Torzoni, Andrea Manzoni, Stefano Mariani
In the context of structural health monitoring (SHM), the selection and extraction of damage-sensitive features from raw sensor recordings represent a critical step towards solving…
Mastering truss structure optimization with tree search
Gabriel Garayalde, Luca Rosafalco, Matteo Torzoni +1
This study investigates the combined use of generative grammar rules and Monte Carlo Tree Search (MCTS) for optimizing truss structures. Our approach accommodates intermediate cons…