Framework for Quantification and Risk Analysis for Layered Uncertainty using Optimization: NASA UQ Challenge

Abstract

The focus of this work is on uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design. This paper is being submitted to the special session, NASA Langley Multidisciplinary Uncertainty Quantification Challenge. For uncertainty characterization, an MCMC based Bayesian approach and a CDF Matching method are compared and found to give similar results; thus increasing our confidence in the methods. A surrogate based p-box sensitivity analysis method is employed to identify the most sensitive parameters to the risk analysis metrics. Uncertainty propagation to find extreme values for the risk analysis metrics are done using a single loop importance sampling (Efficient Reliability Reanalysis) and a double loop surrogates based method. The use of surrogates and importance sampling was dictated by the cost of the black-box functions provided by NASA. A simpler toy problem is also devised to mimic the NASA problem, which helped us in thoroughly testing our methods and their repeatability.

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