Marco Tezzele
Orcid: 0000-0001-9747-6328
According to our database1,
Marco Tezzele
authored at least 50 papers
between 2018 and 2025.
Collaborative distances:
Collaborative distances:
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2025
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Online presence:
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Bibliography
2025
J. Comput. Appl. Math., 2025
2024
A Local Approach to Parameter Space Reduction for Regression and Classification Tasks.
J. Sci. Comput., June, 2024
Data-driven parameterization refinement for the structural optimization of cruise ship hulls.
CoRR, 2024
2023
Adv. Model. Simul. Eng. Sci., December, 2023
2022
A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls.
CoRR, 2022
2021
ATHENA: Advanced Techniques for High dimensional parameter spaces to Enhance Numerical Analysis.
Softw. Impacts, 2021
A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems.
SIAM J. Sci. Comput., 2021
A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition.
CoRR, 2021
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering.
CoRR, 2021
Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing.
CoRR, 2021
2020
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces.
CoRR, 2020
Kernel-based Active Subspaces with application to CFD parametric problems using Discontinuous Galerkin method.
CoRR, 2020
On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis.
CoRR, 2020
Enhancing CFD predictions in shape design problems by model and parameter space reduction.
Adv. Model. Simul. Eng. Sci., 2020
2019
Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations.
CoRR, 2019
A non-intrusive approach for proper orthogonal decomposition modal coefficients reconstruction through active subspaces.
CoRR, 2019
Efficient Reduction in Shape Parameter Space Dimension for Ship Propeller Blade Design.
CoRR, 2019
2018
Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems.
Adv. Model. Simul. Eng. Sci., 2018