170 citations · 763 across the 30 of their papers we have counts for
30 papers
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…
PyDMD: A Python package for robust dynamic mode decomposition
Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo +5
The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's l…
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…
A DeepONet multi-fidelity approach for residual learning in reduced order modeling
Nicola Demo, Marco Tezzele, Gianluigi Rozza
In the present work, we introduce a novel approach to enhance the precision of reduced order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models provide…
A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls
Marco Tezzele, Lorenzo Fabris, Matteo Sidari +2
Nowadays, the shipbuilding industry is facing a radical change towards solutions with a smaller environmental impact. This can be achieved with low emissions engines, optimized sha…
A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition
Eleonora Donadini, Maria Strazzullo, Marco Tezzele +1
This work recasts time-dependent optimal control problems governed by partial differential equations in a Dynamic Mode Decomposition with control framework. Indeed, since the numer…
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Francesco Romor, Marco Tezzele, Markus Mrosek +2
Multi-fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the ap…
A local approach to parameter space reduction for regression and classification tasks
Francesco Romor, Marco Tezzele, Gianluigi Rozza
Parameter space reduction has been proved to be a crucial tool to speed-up the execution of many numerical tasks such as optimization, inverse problems, sensitivity analysis, and s…
ATHENA: Advanced Techniques for High Dimensional Parameter Spaces to Enhance Numerical Analysis
Francesco Romor, Marco Tezzele, Gianluigi Rozza
ATHENA is an open source Python package for reduction in parameter space. It implements several advanced numerical analysis techniques such as Active Subspaces (AS), Kernel-based A…
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…
Kernel-based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method
Francesco Romor, Marco Tezzele, Andrea Lario +1
Nonlinear extensions to the active subspaces method have brought remarkable results for dimension reduction in the parameter space and response surface design. We further develop a…
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…
A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems
Nicola Demo, Marco Tezzele, Gianluigi Rozza
In this work, we present an extension of the genetic algorithm (GA) which exploits the supervised learning technique called active subspaces (AS) to evolve the individuals on a low…
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…
Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations
Gianluigi Rozza, Martin Hess, Giovanni Stabile +2
We provide first the functional analysis background required for reduced order modeling and present the underlying concepts of reduced basis model reduction. The projection-based m…
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…
Efficient Reduction in Shape Parameter Space Dimension for Ship Propeller Blade Design
Andrea Mola, Marco Tezzele, Mahmoud Gadalla +4
In this work, we present the results of a ship propeller design optimization campaign carried out in the framework of the research project PRELICA, funded by the Friuli Venezia Giu…
A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems
Nicola Demo, Marco Tezzele, Andrea Mola +1
In the reduced order modeling (ROM) framework, the solution of a parametric partial differential equation is approximated by combining the high-fidelity solutions of the problem at…
Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces
Marco Tezzele, Nicola Demo, Gianluigi Rozza
We propose a numerical pipeline for shape optimization in naval engineering involving two different non-intrusive reduced order method (ROM) techniques. Such methods are proper ort…
Advances in Reduced Order Methods for Parametric Industrial Problems in Computational Fluid Dynamics
Gianluigi Rozza, Haris Malik, Nicola Demo +4
Reduced order modeling has gained considerable attention in recent decades owing to the advantages offered in reduced computational times and multiple solutions for parametric prob…
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…