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Advances in the sequential design of computer experiments based on active learning. (English) Zbl 1318.62264

Summary: We present some advances in the design of computer experiments. A Gaussian Process (GP) model is fitted to the computer experiment data as a surrogate model. We investigate using the Active Learning (AL) strategy of finding design points that maximize reduction on predictive variance. Using a series of approximations based on standard results from linear algebra (Weyl’s inequalities), we establish a score that approximates the AL utility. Our method is illustrated with a simulated example as well as with an intermediate climate computer model.

MSC:

62L05 Sequential statistical design
60G15 Gaussian processes
65C60 Computational problems in statistics (MSC2010)
62P12 Applications of statistics to environmental and related topics

Software:

Rtwalk; t-walk
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Full Text: DOI

References:

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