A quantile-based method to efficiently determine model response at boundary probabilities

Abstract

Quantiles are values of a function associated with specific probabilities. Two very specific quantiles, that is, ±3, are usually used for estimating probabilistic distribution of the function. Aiming at the engineering complex implicit function, the article provides an algorithm for calculating these values efficiently and rapidly. First, build kriging model with initial high-efficiency samples and the model is taken as the approximate initial analytical expression of the original function. Then, seek for the approximate most probable point with kriging model and performance measure approach. The most probable points found every time and their corresponding function responses are taken as new samples to reconstruct the built kriging model. After several times of reconstructing kriging model, the convergence value for the corresponding quantile can be obtained. Finally, the illustrative cases in this article demonstrate that the quantile-based method can provide accurate results with high efficiency and in this way, the quantile convergence value can be obtained by a few samples.