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Calibrating Simulation Models Using the Knowledge Gradient with Continuous Parameters
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jain, Shweta Montoya-Torres, J. Hugan, Joseph Yücesan, Enver |
| Copyright Year | 2010 |
| Abstract | We describe an adaptation of the knowledge gradient, originally developed for discrete ranking and selection problems, to the problem of calibrating continuous parameters for the purpose of tuning a simulator. The knowledge gradient for continuous parameters uses a continuous approximation of the expected value of a single measurement to guide the choice of where to collect information next. We show how to find the parameter setting that maximizes the expected value of a measurement by optimizing a continuous but nonconcave surface. We compare the method to sequential kriging for a series of test surfaces, and then demonstrate its performance in the calibration of an expensive industrial simulator. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://castlelab.princeton.edu/html/Papers/ScottPowellSimao-WSC2010.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |