Scalability on LHS samples for use in uncertainty analysis of large numerical models

摘要

The present paper deals with the utilization of advanced sampling statistical methods to perform uncertainty and sensitivity analysis on numerical models. Such models may represent physcial phenomena, logical structures (such as boolean expressions) or other systems, and various of their intrinsic parameters and/or input variables are usually treated as random variables simultaneously. In the present paper a simple method to scale-up LHS samples is presented, starting with a small sample and duplicating its size at each step, making it possible to re-use the already run numerical model results, obtained with the smaller sample. The method does not distort the statistical properties of the random variables and does not add any bias to the samples. The result is that a significant reduction in numerical models running time can be achieved (by re-using the previously run samples), keeping all the advantages of LHS, until an acceptable representation level is achieved in the output variables.