Hybrid Bayesian Solution to NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge

This paper presents a hybrid Bayesian solution to the multidisciplinary uncertainty quantification challenge posed by NASA Langley Research Center. The hybrid approach builds probabilistic models of the outputs by combining the simulations and “experimental” observations provided by NASA. The proposed method is a synergy between Gaussian process surrogate modeling and Bayesian principles, and the resulting tool allows construction of surrogate models, parameter calibration, and variance-based global sensitivity analysis. Besides Sobol indices, a cumulative-density-function-based sensitivity to the probability of failure is also explored. Particle swarm optimization has been used in conjunction with the Gaussian process surrogates to find extreme values of both the mean value of the worst-case requirement function and the failure probability. The capability of the proposed techniques is demonstrated on the four subproblems of uncertainty characterization, sensitivity analysis, uncertainty propagation, and ...