13 citations · 24 across the 4 of their papers we have counts for
4 papers
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…
Adaptive surrogates of crashworthiness models for multi-purpose engineering analyses accounting for uncertainty
Marc Rocas, Alberto García-González, Xabier Larrayoz +1
Uncertainty Quantification (UQ) is a booming discipline for complex computational models based on the analysis of robustness, reliability and credibility. UQ analysis for nonlinear…
Nonintrusive Uncertainty Quantification for automotive crash problems with VPS/Pamcrash
Marc Rocas, Alberto García-González, Sergio Zlotnik +2
Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex systems, enhancing reliability of engineering simulations. In crashworthiness, having an a…
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…