Towards the NASA UQ Challenge 2019: Systematically forward and inverse approaches for uncertainty propagation and quantification. (15th February 2022) - Bi, Sifeng; He, Kui; Zhao, Yanlin; Moens, David; Beer, Michael; Zhang, Jingrui
Highlights: A dedicated paper addressing the NASA Uncertainty Quantification Challenge 2019. Subproblems are solved according their forward or inverse features. Decoupling two-loop uncertainty propagation approach for forward procedures. Bayesian framework with statistical calibration metric for inverse procedures. Parallel computation and adaptive surrogate optimization to reduce calculation cost. Abstract: This paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by proposing a series of approaches for both forward and inverse treatment of uncertainty propagation and quantification. The primary effort is placed on the categorization of the subproblems as to be forward or inverse procedures, such that dedicated techniques are proposed for the two directions, respectively. The sensitivity analysis and reliability analysis are categorized as forward procedures, while modal calibration & uncertainty reduction, reliability-based optimization, and risk-based design are regarded as inverse procedures. For both directions, the overall approach is based on imprecise probability characterization where both aleatory and epistemic uncertainties are investigated for the inputs, and consequently, the output is described as the probability-box (P-box). Theoretic development is focused on the definition of comprehensive uncertainty quantification criteria from limited and irregular time-domain observations to extract as much as possible uncertainty information, which will be significant for the inverse procedure to refine uncertainty models. Furthermore, a decoupling approach is proposed to investigate the P-box along two directions such that the epistemic and aleatory uncertainties are decoupled, and thus a two-loop procedure is designed to propagate both epistemic and aleatory uncertainties through the systematic model. The key for successfully addressing this challenge is in obtaining on the balance among an appropriate hypothesis of the input uncertainty model, a comprehensive criterion of output uncertainty quantification, and a computational viable approach for both forward and inverse uncertainty treatment.