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Uncertainty analysis in the outcomes of model predictions is a key element indecision-based material design to establish confidence in the models andevaluate the fidelity of models. Uncertainty Propagation (UP) is a technique todetermine model output uncertainties based on the uncertainty in its inputvariables. The most common and simplest approach to propagate the uncertaintyfrom a model inputs to its outputs is by feeding a large number of samples tothe model, known as Monte Carlo (MC) simulation which requires exhaustivesampling from the input variable distributions. However, MC simulations areimpractical when models are computationally expensive. In this work, weinvestigate the hypothesis that while all samples are useful on average, somesamples must be more useful than others. Thus, reordering MC samples andpropagating more useful samples can lead to enhanced convergence in statisticsof interest earlier and thus, reducing the computational burden of UP process.Here, we introduce a methodology to adaptively reorder MC samples and show howit results in reduction of computational expense of UP processes.

Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples