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Communication Dans Un Congrès Année : 2014

An Improvement of a Metamodel-Based Importance Sampling Algorithm for Estimating Small Failure Probabilities

Résumé

This work presents an improvement to a Monte Carlo-based algorithm of literature for the estimation of small failure probabilities. The original algorithm of literature is based on the implementation of the optimal importance density by a surrogate, kriging-based metamodel approximating the response function which determines the failure by reference with a given threshold. The significant improvement of the algorithm is obtained by a more effective training of the metamodel, and allows for a further decrease of the computational efforts required in the failure probability estimation. The performance of the new algorithm is demonstrated on a few analytic examples of literatur
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Dates et versions

hal-04366320 , version 1 (28-12-2023)

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Citer

F. Cadini, A. Gioletta, E. Zio. An Improvement of a Metamodel-Based Importance Sampling Algorithm for Estimating Small Failure Probabilities. 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014, Jul 2014, unknown, France. pp.2104-2114, ⟨10.1061/9780784413609.211⟩. ⟨hal-04366320⟩
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