Density modification-based reliability sensitivity analysis
By Paul Lemaître, E. Sergienko, A. Arnaud, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss. 2012
68 citations recorded by the source corpus.
Sensitivity analysis (SA) of a numerical model, for instance simulating physical phenomena, is useful to quantify the influence of the inputs on the model responses. This paper proposes a new sensitivity index, based upon the modification of the probability density function (pdf) of the random inputs, when the quantity of interest is a failure probability (probability that a model output exceeds a given threshold). An input is considered influential if the input pdf modification leads to a broad change in the failure probability. These sensitivity indices can be computed using the sole set of simulations that has already been used to estimate the failure probability, thus limiting the number of calls to the numerical model. In the case of a Monte Carlo sample, asymptotical properties of the indices are derived. Based on Kullback–Leibler divergence, several types of input perturbations are introduced. The relevance of this new SA method is analysed through three case studies.
Subfields: Mechanics of Materials, Safety, Risk, Reliability and Quality, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c498e36e3a5a72c8cc394fd1edc37f1ab4f91f97
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In Mechanics of Materials
In Safety, Risk, Reliability and Quality
In Statistics and Probability
Citations in the corpus
- A Review on Global Sensitivity Analysis Methods (2014, background): “[53]) or a quantile estimation (Cannamela et al.”
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “Specific SA methods have been successfully applied to explain the influential factors on decision boundaries and robustness analysis (e.g., Lemaître et al., 2015; Sueur et al., 2017; Bachoc et al., 2020a; Gauchy et al., 2020).”