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Effective Design for Sobol Indices Estimation Based on Polynomial Chaos Expansions

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Book cover Conformal and Probabilistic Prediction with Applications (COPA 2016)

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Abstract

Sobol’ indices are a common metric of dependency in sensitivity analysis. It is used as a measure of confidence of input variables influence on the output of the analyzed mathematical model. We consider a problem of selection of experimental design points for Sobol’ indices estimation. Based on the concept of D-optimality, we propose a method for constructing an adaptive design of experiments, effective for the calculation of Sobol’ indices from Polynomial Chaos Expansions. We provide a set of applications that demonstrate the efficiency of the proposed approach.

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Acknowledgments

The research was conducted in IITP RAS and supported solely by the Russian Science Foundation grant (project 14-50-00150).

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Authors and Affiliations

  1. Kharkevich Institute for Information Transmission Problems, Bolshoy Karetny per. 19, Moscow, 127994, Russia

    Evgeny Burnaev & Ivan Panin

  2. Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland

    Bruno Sudret

Authors
  1. Evgeny Burnaev
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  2. Ivan Panin
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  3. Bruno Sudret
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Corresponding author

Correspondence to Ivan Panin .

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Editors and Affiliations

  1. University of London, Egham, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, United Kingdom

    Zhiyuan Luo

  3. CIEMAT, Madrid, Spain

    Jesús Vega

  4. University of London, Egham, United Kingdom

    Vladimir Vovk

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Burnaev, E., Panin, I., Sudret, B. (2016). Effective Design for Sobol Indices Estimation Based on Polynomial Chaos Expansions. In: Gammerman, A., Luo, Z., Vega, J., Vovk, V. (eds) Conformal and Probabilistic Prediction with Applications. COPA 2016. Lecture Notes in Computer Science(), vol 9653. Springer, Cham. https://doi.org/10.1007/978-3-319-33395-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-33395-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33394-6

  • Online ISBN: 978-3-319-33395-3

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