Inverse uncertainty propagation for demand driven data acquisition

Baumgärtel, Philipp; Endler, Gregor; Wahl, Andreas M.; Lenz, Richard

doi:10.1109/wsc.2014.7019934

Cited by 4 publications

(1 citation statement)

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“…Chen et al [8] reported that inverse uncertainty propagation methods can be used as an enabler to solve the uncertainty allocation problem. Although several methods have been developed for inverse uncertainty propagation (e.g., the Gaussian process [9], Karhunen-Loève Expansion [10], [11], Polynomial Chaos [12], and maximum likelihood [13]) these may lead to relatively high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Interactive Uncertainty Allocation and Tradeoff for Early Stage Design of Complex Systems

et al. 2020

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A common probabilistic approach to perform uncertainty allocation is to assign acceptable variability in the sources of uncertainty, such that pre-specified probabilities of meeting performance constraints are satisfied. However, the computational cost of obtaining the associated tradeoffs increases significantly when more sources of uncertainty and more outputs are considered. Consequently, visualizing and exploring the decision (trade) space becomes increasingly difficult, which, in turn, makes the decisionmaking process cumbersome for practicing designers. To address this problem, proposed is a parameterization of the input probability distribution functions, to account for several statistical moments. This, combined with efficient uncertainty propagation and inverse computation techniques, results in a computational system that performs order(s) of magnitude faster than a state-of-the-art optimization technique. The approach is demonstrated by means of an illustrative example and a representative aircraft thermal system integration example.

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