Publication de 6 numéros par an
ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099
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ADAPTIVE SELECTION OF SAMPLING POINTS FOR UNCERTAINTY QUANTIFICATION
RÉSUMÉ
We present a simple and robust strategy for the selection of sampling points in uncertainty quantification. The goal is to achieve the fastest possible convergence in the cumulative distribution function of a stochastic output of interest. We assume that the output of interest is the outcome of a computationally expensive nonlinear mapping of an input random variable, whose probability density function is known. We use a radial function basis to construct an accurate interpolant of the mapping. This strategy enables adding new sampling points one at a time, adaptively. This takes into full account the previous evaluations of the target nonlinear function. We present comparisons with a stochastic collocation method based on the Clenshaw-Curtis quadrature rule, and with an adaptive method based on hierarchical surplus, showing that the new method often results in a large computational saving.
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Camporeale E., Chu X., Agapitov O. V., Bortnik J., On the Generation of Probabilistic Forecasts From Deterministic Models, Space Weather, 17, 3, 2019. Crossref