Kriging-sparse Polynomial Dimensional Decomposition surrogate model with adaptive refinement
CORTESI, Andrea Francesco
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
CONGEDO, Pietro Marco
Shape reconstruction and identification [DeFI]
Centre de Mathématiques Appliquées de l'Ecole polytechnique [CMAP]
Shape reconstruction and identification [DeFI]
Centre de Mathématiques Appliquées de l'Ecole polytechnique [CMAP]
CORTESI, Andrea Francesco
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
CONGEDO, Pietro Marco
Shape reconstruction and identification [DeFI]
Centre de Mathématiques Appliquées de l'Ecole polytechnique [CMAP]
< Reduce
Shape reconstruction and identification [DeFI]
Centre de Mathématiques Appliquées de l'Ecole polytechnique [CMAP]
Language
en
Article de revue
This item was published in
Journal of Computational Physics. 2019-03-01, vol. 380, p. 212-242
Elsevier
Date
2019-03-01English Abstract
Uncertainty Quantification and Sensitivity Analysis problems are made more difficult in the case of applications involving expensive computer simulations. This is because a limited amount of simulations is available to ...Read more >
Uncertainty Quantification and Sensitivity Analysis problems are made more difficult in the case of applications involving expensive computer simulations. This is because a limited amount of simulations is available to build a sufficiently accurate metamodel of the quantities of interest.In this work, an algorithm for the construction of a low-cost and accurate metamodel is proposed, having in mind computationally expensive applications. It has two main features. First, Universal Kriging is coupled with sparse Polynomial Dimensional Decomposition (PDD) to build a metamodel with improved accuracy. The polynomials selected by the adaptive PDD representation are used as a sparse basis to build a Universal Kriging surrogate model. Secondly, a numerical method, derived from anisotropic mesh adaptation, is formulated in order to adaptively insert a fixed number of new training points to an existing Design of Experiments.The convergence of the proposed algorithm is analyzed and assessed on different test functions with an increasing size of the input space. Finally, the algorithm is used to propagate uncertainties in two high-dimensional real problems related to the atmospheric reentry.Read less <
English Keywords
Surrogate modeling
Universal kriging
Sparse polynomial dimensional decomposition
Anisotropic adaptive meshing
Adaptive refinement
Origin
Hal imported