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Accession Number ADA567849
Title Novel Mathematical and Computational Techniques for Robust Uncertainty Quantification.
Publication Date Jun 2011
Media Count 10p
Personal Author D. Gottlieb J. Hesthaven P. Dupuis
Abstract Uncertainty quantification refers to a broad set of techniques for understanding the impact of uncertainties in complicated mechanical and physical systems. In this context 'uncertainty' can take on many meanings. Aleatoric uncertainty refers to inherent uncertainty due to stochastic or probabilistic variability. This type of uncertainty is irreducible in that there will always be positive variance since the underlying variables are truly random. Epistemic uncertainty refers to limited knowledge we may have about the model or system. This type of uncertainty is reducible in that if we have more information, e.g., take more measurements, then this type of uncertainty can be reduced. For many problems where uncertainty quantification is important, the acquisition of data is difficult or expensive. The epistemic uncertainty cannot be removed entirely, and so one needs modeling and computational techniques which can also accommodate this form of uncertainty.
Keywords Aleatoric uncertainty
Algorithms
Approximation(Mathematics)
Computation science
Computational probability
Epistemic uncertainty
Monte carlo method
Stochastic processes
Uncertainty
Uncertainty quantification


 
Source Agency Non Paid ADAS
NTIS Subject Category 72F - Statistical Analysis
72E - Operations Research
Corporate Author Brown Univ., Providence, RI. Div. of Applied Mathematics.
Document Type Technical report
Title Note Final rept. 1 Jul 2007-30 Nov 2010.
NTIS Issue Number 1310
Contract Number FA9550-07-1-0544

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