Search Results for author:
Found 4 papers, 2 papers with code
Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
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.
A local approach to parameter space reduction for regression and classification tasks
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.
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
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.
Kernel-based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method
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
