From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information.

• A comprehensive framework for engineering design under uncertainty. • Focus on limited and partial information, time series, and black-box models. • Calibration, sensitivity analysis and RBDO performed with mixed uncertainty. • Efficient sampling of complex uncertainty models using sliced normal distributions. • Framework is demonstrated on the 2020 NASA challenge on design under uncertainty. In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application. [ABSTRACT FROM AUTHOR]