30 citations · 82 across the 7 of their papers we have counts for
5 papers · 1 filter
Nonlinear dimensionality reduction for parametric problems: a kernel Proper Orthogonal Decomposition (kPOD)
Pedro Díez, Alba Muixí, Sergio Zlotnik +1
Reduced-order models are essential tools to deal with parametric problems in the context of optimization, uncertainty quantification, or control and inverse problems. The set of pa…
Nonintrusive reduced order model for parametric solutions of inertia relief problems
F. Cavaliere, S. Zlotnik, R. Sevilla +2
The Inertia Relief (IR) technique is widely used by industry and produces equilibrated loads allowing to analyze unconstrained systems without resorting to the more expensive full…
A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy
Alberto García-González, Antonio Huerta, Sergio Zlotnik +1
Methodologies for multidimensionality reduction aim at discovering low-dimensional manifolds where data ranges. Principal Component Analysis (PCA) is very effective if data have li…
Parametric solutions involving geometry integrated with computer-aided design
Ruben Sevilla, Sergio Zlotnik, Antonio Huerta
The main objective of this work is to describe a general and original approach for computing an off-line solution for a set of parameters describing the geometry of the domain. Tha…
Generalized parametric solutions in Stokes flow
Pedro Diez, Sergio Zlotnik, Antonio Huerta
Design optimization and uncertainty quantification, among other applications of industrial interest, require fast or multiple queries of some parametric model. The Proper Generaliz…