Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models

  title={Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models},
  author={X. Zhu and Bruno Sudret},
  journal={Reliab. Eng. Syst. Saf.},
  • X. Zhu, B. Sudret
  • Published 4 May 2020
  • Computer Science, Mathematics
  • Reliab. Eng. Syst. Saf.
Global sensitivity analysis aims at quantifying the impact of input variability onto the variation of the response of a computational model. It has been widely applied to deterministic simulators, for which a set of input parameters has a unique corresponding output value. Stochastic simulators, however, have intrinsic randomness due to their use of (pseudo)random numbers, so they give different results when run twice with the same input parameters but non-common random numbers. Due to this… 
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  • X. Zhu, B. Sudret
  • Computer Science, Mathematics
    SIAM/ASA Journal on Uncertainty Quantification
  • 2021
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