170 citations · 226 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
Online learning in bifurcating dynamic systems via SINDy and Kalman filtering
Luca Rosafalco, Paolo Conti, Andrea Manzoni +2
We propose the use of the Extended Kalman Filter (EKF) for online data assimilation and update of a dynamic model, preliminary identified through the Sparse Identification of Nonli…
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…
EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics
Luca Rosafalco, Paolo Conti, Andrea Manzoni +2
Measured data from a dynamical system can be assimilated into a predictive model by means of Kalman filters. Nonlinear extensions of the Kalman filter, such as the Extended Kalman…
Multi-fidelity reduced-order surrogate modeling
Paolo Conti, Mengwu Guo, Andrea Manzoni +3
High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations…
A digital twin framework for civil engineering structures
Matteo Torzoni, Marco Tezzele, Stefano Mariani +2
The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced li…