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Quantifying Uncertainty in Simulations of Complex Engineered Systems

Friday, September 9, 3:30PM – 5PM
ACE 6.304

Robert Moser, ICES, PECOS

Computational simulation is a ubiquitous tool in engineering. Further, the explosion of computational capabilities over the last several decades has resulted in the use of computational models of unprecedented complexity to make critical design and operation decisions. One potential benefit should be to improve reliability of the engineered system while reducing margins, due to the more accurate predictions such models could produce. However, realizing this benefit requires reliable estimates of the uncertainties in the predictions. The Center for Predictive Engineering and COmputation Sciences (PECOS) at the University of Texas at Austin is developing tools and methodologies for quantifying uncertainties in such simulations. Among the issues being addressed are calibration and validation of physics-based models using uncertain data, characterizing the uncertainties in such data, representing uncertainty due to model inadequacy and validating predictions of unobserved quantities. At PECOS, these issues are addressed by representing uncertainties as Bayesian probabilities, with calibration and validation processes formulated in terms of Bayesian inference.

In this talk, the PECOS approach to uncertainty quantification in complex systems will be discussed with example applications to the prediction of reentry vehicles with ablative heat shields.

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