170 citations · 226 across the 12 of their papers we have counts for
4 papers · 1 filter
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…