13 citations · 24 across the 5 of their papers we have counts for
3 papers · 1 filter
Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives
Matteo Giacomini, Pedro Díez
Surrogate models provide compact relations between user-defined input parameters and output quantities of interest, enabling the efficient evaluation of complex parametric systems…
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…
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…