Enhancing Stochastic Kriging Metamodels with Stochastic Gradient Estimators

Duration: 30 mins 27 secs

About this item

Description:	Nelson, B; Ankenman, B (Northwestern University) Wednesday 07 September 2011, 11:30-12:00

Created:

2011-09-14 14:39

Collection:

Design and Analysis of Experiments

Publisher:

Isaac Newton Institute

Nelson, B; Ankenman, B

Language:

eng (English)

Credits:

Producer:

Steve Greenham


Abstract:	Stochastic kriging is the natural extension of kriging metamodels for the design and analysis of computer experiments to the design and analysis of stochastic simulation experiments where response variance may differ substantially across the design space. In addition to estimating the mean response, it is sometimes possible to obtain an unbiased or consistent estimator of the response-surface gradient from the same simulation runs. However, like the response itself, the gradient estimator is noisy. In this talk we present methodology for incorporating gradient estimators into response surface prediction via stochastic kriging, evaluate its effectiveness in improving prediction, and specifically consider two gradient estimators: the score function/likelihood ratio method and infinitesimal perturbation analysis.

Abstract:

Stochastic kriging is the natural extension of kriging metamodels for the design and analysis of computer experiments to the design and analysis of stochastic simulation experiments where response variance may differ substantially across the design space. In addition to estimating the mean response, it is sometimes possible to obtain an unbiased or consistent estimator of the response-surface gradient from the same simulation runs. However, like the response itself, the gradient estimator is noisy. In this talk we present methodology for incorporating gradient estimators into response surface prediction via stochastic kriging, evaluate its effectiveness in improving prediction, and specifically consider two gradient estimators: the score function/likelihood ratio method and infinitesimal perturbation analysis.

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