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- W2032177029 abstract "Error measures are important for assessing uncertainty in surrogate predictions. We use a suite of test problems to appraise a few error estimations for polynomial response surface and kriging. In addition, we study the performance of cross-validation error measures that can be used with any surrogate. We use a large number of experimental designs to obtain the variability of error estimates with respect to experimental designs for each problem. We find that the (actual) errors for polynomial response surfaces are less sensitive to the choice of experimental designs than kriging errors. This is attributed to the variability in the maximum likelihood estimates of the kriging parameters. We find that no single error measure outperforms other measures on all test problems. Computationally expensive integrated local error measures (standard error for polynomials and mean square error for kriging) estimate the actual root mean square error very well. The distribution-free cross-validation error characterized actual errors reasonably well. While estimated root mean square error for polynomial response surface is a good estimate of the actual errors, process variance for kriging is not. We explore methods of simultaneously using multiple error measures and demonstrate that the geometric means of combinations of multiple error measures improve the assessment of the actual errors compared to the individual error measures." @default.
- W2032177029 created "2016-06-24" @default.
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- W2032177029 date "2008-01-07" @default.
- W2032177029 modified "2023-09-26" @default.
- W2032177029 title "Error Measures for Noise-Free Surrogate Approximations" @default.
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- W2032177029 doi "https://doi.org/10.2514/6.2008-901" @default.
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