Confidence intervals for sensitivity indices using reduced-basis metamodels

Alexandre Janon 1, 2, * Maëlle Nodet 1 Clémentine Prieur 1, 2
* Corresponding author
1 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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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https://hal.inria.fr/inria-00567977
Contributor : Alexandre Janon <>
Submitted on : Tuesday, February 22, 2011 - 1:33:15 PM
Last modification on : Tuesday, July 9, 2019 - 3:14:03 PM
Long-term archiving on: Tuesday, November 6, 2012 - 2:40:59 PM

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  • HAL Id : inria-00567977, version 1
  • ARXIV : 1102.4668

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Alexandre Janon, Maëlle Nodet, Clémentine Prieur. Confidence intervals for sensitivity indices using reduced-basis metamodels. 2011. ⟨inria-00567977v1⟩

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