QUANTIFYING UNCERTAIN SYSTEM OUTPUTS VIA THE MULTI-LEVEL MONTE CARLO METHOD-DISTRIBUTION AND ROBUSTNESS MEASURES

نویسندگان

چکیده

In this work, we consider the problem of estimating probability distribution, quantile or conditional expectation above quantile, so called conditional-value-at-risk, output quantities complex random differential models by MLMC method. We follow approach (reference), which recasts estimation to computation suitable parametric expectations. present novel computable error estimators for such quantities, are then used optimally tune hierarchy in a continuation type adaptive algorithm. demonstrate efficiency and robustness our continuation-MLMC an array numerical test cases.

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ژورنال

عنوان ژورنال: International Journal for Uncertainty Quantification

سال: 2023

ISSN: ['2152-5080', '2152-5099']

DOI: https://doi.org/10.1615/int.j.uncertaintyquantification.2023045259