A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems
2019-01-01 Demo, Nicola; Tezzele, Marco; Mola, Andrea; Rozza, Gianluigi
A continuous convolutional trainable filter for modelling unstructured data
2023-01-01 Coscia, D; Meneghetti, L; Demo, N; Stabile, G; Rozza, G
A dimensionality reduction approach for convolutional neural networks
2023-01-01 Meneghetti, L; Demo, N; Rozza, G
A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics
2022-01-01 Ortali, Giulio; Demo, Nicola; Rozza, Gianluigi
A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces
2019-01-01 Demo, Nicola; Tezzele, Marco; Rozza, Gianluigi
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
2022-01-01 Meneghetti, L.; Demo, N.; Rozza, G.
A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems
2021-01-01 Demo, N.; Tezzele, M.; Rozza, G.
Advances in reduced order methods for parametric industrial problems in computational fluid dynamics
2018-01-01 Rozza, Gianluigi; Malik, Haris; Demo, Nicola; Tezzele, Marco; Girfoglio, Michele; Stabile, Giovanni; Mola, Andrea
An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
2021-01-01 Demo, Nicola; Ortali, Giulio; Gustin, Gianluca; Rozza, Gianluigi; Lavini, Gianpiero
An efficient shape parametrisation by free-form deformation enhanced by active subspace for hull hydrodynamic ship design problems in open source environment
2018-01-01 Demo, Nicola; Tezzele, Marco; Mola, Andrea; Rozza, Gianluigi
An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics
2022-01-01 Tezzele, Marco; Demo, Nicola; Mola, Andrea; Rozza, Gianluigi
Chapter 20: A Deep Learning Approach to Improving Reduced Order Models
2022-01-01 Meneghetti, Laura; Shah, Nirav; Girfoglio, Michele; Demo, Nicola; Tezzele, Marco; Lario, Andrea; Stabile, Giovanni; Rozza, Gianluigi
Enhancing CFD predictions in shape design problems by model and parameter space reduction
2020-01-01 Tezzele, M.; Demo, N.; Stabile, G.; Mola, A.; Rozza, G.
Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing
2021-01-01 Demo, Nicola; Tezzele, Marco; Mola, Andrea; Rozza, Gianluigi
Increasing speedup of naval hull workflow optimization using Reduced Order Methods
2016-12-16 Demo, Nicola
Model order reduction by means of active subspaces and dynamic mode decomposition for parametric hull shape design hydrodynamics
2018-01-01 Tezzele, Marco; Demo, Nicola; Gadalla, Mahmoud Gamal Ali Salem; Mola, Andrea; Rozza, Gianluigi
PyGeM: Python Geometrical Morphing
2021-01-01 Tezzele, M.; Demo, N.; Mola, A.; Rozza, G.
Reduced order isogeometric analysis approach for PDEs in parametrized domains
2020-01-01 Garotta, F.; Demo, N.; Tezzele, M.; Carraturo, M.; Reali, A.; Rozza, G.
Shape Optimization by means of Proper Orthogonal Decomposition and Dynamic Mode Decomposition
2018-01-01 Demo, Nicola; Tezzele, Marco; Gustin, Gianluca; Lavini, Gianpiero; Rozza, Gianluigi
Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces
2019-01-01 Tezzele, Marco; Demo, Nicola; Rozza, Gianluigi