48 citations · 60 across the 3 of their papers we have counts for
10 papers · 1 filter
Digital Twins in Coronary Artery Disease: A Mathematical Roadmap
Alessandro Veneziani, Annalisa Quaini, Marco Tezzele +2
The combination of data and models, enhanced by AI methodologies, leads to the paradigm called Digital Twins. This concept is expected to bring unprecedented support to personalize…
Data-driven parameterization refinement for the structural optimization of cruise ship hulls
Lorenzo Fabris, Marco Tezzele, Ciro Busiello +2
In this work, we focus on the early design phase of cruise ship hulls, where the designers are tasked with ensuring the structural resilience of the ship against extreme waves whil…
Data-driven Discovery of Delay Differential Equations with Discrete Delays
Alessandro Pecile, Nicola Demo, Marco Tezzele +2
The Sparse Identification of Nonlinear Dynamics (SINDy) framework is a robust method for identifying governing equations, successfully applied to ordinary, partial, and stochastic…
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…
Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
Nicola Demo, Marco Tezzele, Andrea Mola +1
In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a…
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
Francesco Romor, Marco Tezzele, Gianluigi Rozza
Gaussian processes are employed for non-parametric regression in a Bayesian setting. They generalize linear regression, embedding the inputs in a latent manifold inside an infinite…