Efficient Uncertainty Quantification using Gradient-Enhanced Kriging
By Richard P. Dwight, Zhonghua Han. 2009
104 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0b2e4582d1c4b2492fe912f9d7e63fb949e1c441
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Jacques Peter
- Tom D'haene
- Eric Laermans
- Richard P. Dwight
- Ivo Couckuyt
- Selvakumar Ulaganathan
- Francesco Ferranti
In Management Science and Operations Research
- Jacques Peter
- Tom D'haene
- Eric Laermans
- Richard P. Dwight
- Ivo Couckuyt
- Selvakumar Ulaganathan
- Francesco Ferranti
In Statistics and Probability
- Jacques Peter
- Tom D'haene
- Eric Laermans
- Richard P. Dwight
- Ivo Couckuyt
- Selvakumar Ulaganathan
- Francesco Ferranti
- Joaquim R. Martins
Citations in the corpus
- Performance study of multi-fidelity gradient enhanced kriging (2015, background): “Dwight and Han (2009), Laurenceau et al. (2010) and Chung and Alonso (2002) demonstrated that with adjoint formulation k-dimensional gradients can be esti- mated at a computational cost of estimating 1-2 function values at a single sample point.”
- Numerical sensitivity analysis for aerodynamic optimization: A survey of approaches (2010, methodology): “Also if gradients are to be used in a response surface method, for example gradient-enhanced Kriging, then poor gradients will lead to a poor response surface [76].”