no code implementations • 1 Mar 2022 • Francesco Romor, Giovanni Stabile, Gianluigi Rozza
Non-affine parametric dependencies, nonlinearities and advection-dominated regimes of the model of interest can result in a slow Kolmogorov n-width decay, which precludes the realization of efficient reduced-order models based on linear subspace approximations.
1 code implementation • 22 Jul 2021 • Francesco Romor, Marco Tezzele, Gianluigi Rozza
In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to carry out a more efficient dimension reduction in the parameter space.
1 code implementation • 16 Oct 2020 • Francesco Romor, Marco Tezzele, Gianluigi Rozza
We can augment the inputs with the observations of low-fidelity models in order to learn a more expressive latent manifold and thus increment the model's accuracy.
no code implementations • 27 Aug 2020 • Francesco Romor, Marco Tezzele, Andrea Lario, Gianluigi Rozza
Nonlinear extensions to the active subspaces method have brought remarkable results for dimension reduction in the parameter space and response surface design.
Numerical Analysis Numerical Analysis 15A18, 15A60, 41A30, 41A63, 65D15, 65N30