返回Google圖書搜尋

Uncertainty and Sensitivity Analysis for Long-running Computer Codes

其他書名	A Critical Review
出版	Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2010
URL	http://books.google.com.hk/books?id=81ElcgAACAAJ&hl=&source=gbs_api

註釋(Cont.) The second method is specific to GP models, and is based on a Bayesian interpretation of the underlying stochastic process. Finally, to demonstrate the use of these methods, the results from two case studies involving the reliability assessment of passive nuclear safety systems are presented. The general conclusions of this work are that polynomial RSs are frequently incapable of adequately representing the complex input/output behavior exhibited by many mechanistic models. In addition, the goodness-of- fit of the RS should not be misinterpreted as a measure of the predictive capability of the metamodel, since RSs are necessarily biased predictors for deterministic computer models. Furthermore, the extent of this bias is not measured by standard goodness-of-fit metrics (e.g., coefficient of determination, R^2), so these methods tend to provide overly optimistic indications of the quality of the metamodel. The bootstrap procedure does provide indication as to the extent of this bias, with the bootstrap confidence intervals for the RS estimates generally being significantly wider than those of the alternative metamodeling methods. It has been found that the added flexibility afforded by ANNs and GPs can make these methods superior for approximating complex models. In addition, GPs are exact interpolators, which is an important feature when the underlying computer model is deterministic (i.e., when there is no justification for including a random error component in the metamodel). On the other hand, when the number of observations from the computer model is sufficiently large, all three methods appear to perform comparably, indicating that in such cases, RSs can still provide useful approximations.