Name
Abstract | ||
|---|---|---|
Computer simulation often is used to study complex physical and engineering processes. Although a computer simulator often can be viewed as an inexpensive way to gain insight into a system, it still can be computationally costly. Much of the recent work on the design and analysis of computer experiments has focused on scenarios where the goal is to fit a response surface or process optimization. In this article we develop a sequential methodology for estimating a contour from a complex computer code. The approach uses a stochastic process model as a surrogate for the computer simulator. The surrogate model and associated uncertainty are key components in a new criterion used to identify the computer trials aimed specifically at improving the contour estimate. The proposed approach is applied to exploration of a contour for a network queuing system. Issues related to practical implementation of the proposed approach also are addressed. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1198/004017008000000541 | TECHNOMETRICS |
Keywords | Field | DocType |
Computer experiment,Gaussian process,Inverse problem | Computer experiment,Computer simulation,Source code,Surrogate model,Stochastic process,Inverse problem,Queue management system,Statistics,Mathematics,Design of experiments | Journal |
Volume | Issue | ISSN |
50 | 4 | 0040-1706 |
Citations | PageRank | References |
31 | 3.46 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pritam Ranjan | 1 | 66 | 7.73 |
| Derek Bingham | 2 | 126 | 27.32 |
| George Michailidis | 3 | 303 | 35.19 |