48 citations · 48 across the 1 of their papers we have counts for
6 papers
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…
On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis
Mahmoud Gadalla, Marta Cianferra, Marco Tezzele +3
In this work, Dynamic Mode Decomposition (DMD) and Proper Orthogonal Decomposition (POD) methodologies are applied to hydroacoustic dataset computed using Large Eddy Simulation (LE…
Enhancing CFD predictions in shape design problems by model and parameter space reduction
Marco Tezzele, Nicola Demo, Giovanni Stabile +2
In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced…
A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces
Nicola Demo, Marco Tezzele, Gianluigi Rozza
Reduced order modeling (ROM) provides an efficient framework to compute solutions of parametric problems. Basically, it exploits a set of precomputed high-fidelity solutions --- co…
Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains
Fabrizio Garotta, Nicola Demo, Marco Tezzele +3
In this contribution, we coupled the isogeometric analysis to a reduced order modelling technique in order to provide a computationally efficient solution in parametric domains. In…