170 citations · 202 across the 10 of their papers we have counts for
7 papers · 1 filter
Cognitive behavior emerging in mechanical systems through reservoir encoding and active inference
Matteo Torzoni, Domenico Maisto, Andrea Manzoni +3
Mechanical systems are traditionally designed as passive assets, where sensing, analysis, and control are treated as decoupled and externally prescribed processes. Reliance on pred…
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…
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…
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…
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…
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…
Real-time topology optimization via learnable mappings
Gabriel Garayalde, Matteo Torzoni, Matteo Bruggi +1
In traditional topology optimization, the computing time required to iteratively update the material distribution within a design domain strongly depends on the complexity or size…