Confidence intervals for sensitivity indices using reduced-basis metamodels

Alexandre Janon; Maëlle Nodet; Clémentine Prieur

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arXiv · 1102.4668

Confidence intervals for sensitivity indices using reduced-basis metamodels

Alexandre Janon·Maëlle Nodet·Clémentine Prieur

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Abstract

Global sensitivity analysis is often impracticable for complex and time demanding numerical models, as it requires a large number of runs. The reduced-basis approach provides a way to replace the original model by a much faster to run code. In this paper, we are interested in the information loss induced by the approximation on the estimation of sensitivity indices. We present a method to provide a robust error assessment, hence enabling significant time savings without sacrifice on precision and rigourousness. We illustrate our method with an experiment where computation time is divided by a factor of nearly 6. We also give directions on tuning some of the parameters used in our estimation algorithms.

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Alexandre Janon, Maëlle Nodet, Clémentine Prieur. 2011-02-23. Confidence intervals for sensitivity indices using reduced-basis metamodels. https://arxiv.org/abs/1102.4668

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