Gradient-enhanced kriging: Difference between revisions
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Along the lines of |
Along the lines of |
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<ref>{{cite journal | last1 = Wikle | first1 = C.K. | last2 = Berliner | first2 = L.M. | year = 2007 | title = A Bayesian tutorial for data assimilation | url = http://www.sciencedirect.com/science/article/pii/S016727890600354X | journal = Phys. D: Nonlin. Phenom. | volume = 230 | issue = 1-2| pages = 1-16 | doi =}}</ref> |
<ref>{{cite journal | last1 = Wikle | first1 = C.K. | last2 = Berliner | first2 = L.M. | year = 2007 | title = A Bayesian tutorial for data assimilation | url = http://www.sciencedirect.com/science/article/pii/S016727890600354X | journal = Phys. D: Nonlin. Phenom. | volume = 230 | issue = 1-2| pages = 1-16 | doi =}}</ref> |
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<ref name = debaar2014a /> |
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, we are interested in the output <math>x</math> of our computer simulation, for which we assume the [[normal distribution|normal]] [[prior probability|prior probability distribution]]: |
, we are interested in the output <math>x</math> of our computer simulation, for which we assume the [[normal distribution|normal]] [[prior probability|prior probability distribution]]: |
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Revision as of 04:19, 8 November 2016
Template:New unreviewed article Gradient-Enhanced Kriging (GEK) is a surrogate modeling technique used in engineering. A surrogate model (alternatively known as a metamodel, response surface or emulator) is a prediction of the output of an expensive computer code. This prediction is based on a small number of evaluations of the expensive computer code.
Introduction
Predictor equations
In a Bayesian framework, we use Bayes' Theorem to predict the Kriging mean and variance conditional on the observations. In our case, the observations are the results of a number of computer simulations.
Kriging
Along the lines of [2] [1] , we are interested in the output of our computer simulation, for which we assume the normal prior probability distribution:
- ,
with prior mean and prior covariance matrix . The observations have the normal likelihood:
- ,
with the observation matrix and the observation error covariance matrix. After applying Bayes' Theorem we obtain the posterior probability distribution, with Kriging mean:
- E,
and Kriging covariance:
- cov,
where we have the gain matrix:
- .
GEK
Example: Drag coefficient of a transonic airfoil
References
- ^ a b de Baar, J.H.S.; Dwight, R.P.; Bijl, H. (2014). "Improvements to gradient-enhanced Kriging using a Bayesian interpretation". International Journal for Uncertainty Quantification. 4 (3): 205–223.
- ^ Wikle, C.K.; Berliner, L.M. (2007). "A Bayesian tutorial for data assimilation". Phys. D: Nonlin. Phenom. 230 (1–2): 1–16.