The spatial correlation function approach to response surface emission
This is an expository paper which discusses an alternative to conventional response surface methodology for use in simulation experiments where the objective is to express an output variable (response) as a function of several input variables. The method is Bayesian in the sense that uncertainty about the true response function y is expressed by the random function Y, defined on the region of interest X in the space of the input parameters. If Y is Gaussian, straightforward formulas exist for updating y given observations of y(x{sup 1}),y(x{sup 2}),{hor_ellipsis},Y(x{sup n}), which are available from n simulation runs at different settings (x{sup i}{epsilon}X)of the input parameters. The posterior mean of Y(x), viewed as a function of x, serves as the estimated response function {cflx y}. The method is driven primarily by means of a chosen spatial correlation function (SCF), which defines the prior correlation between the responses at any two points in the space of the input parameters. Once the SCF is chosen, the method is naturally adaptive -- {cflx y} becomes more subtle and complex as more simulation runs are made, with no intervention required to add terms to a parametric model. Although much of the focus of this paper is on deterministic simulations, where we have had most of our experience, we shall show how modifications can be made to handle ``random`` responses. Some examples are discussed to illustrate the ideas and the nature of the results.
- Research Organization:
- Oak Ridge National Lab., TN (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 10180390
- Report Number(s):
- CONF-921219--2; ON: DE92040875
- Country of Publication:
- United States
- Language:
- English
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