An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations.
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Carlisle Thacker, W.
Knio, O. M.
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We give an overview of four different ensemble-based techniques for uncertainty quantification and illustrate their application in the context of oil plume simulations. These techniques share the common paradigm of constructing a model proxy that efficiently captures the functional dependence of the model output on uncertain model inputs. This proxy is then used to explore the space of uncertain inputs using a large number of samples, so that reliable estimates of the model’s output statistics can be calculated. Three of these techniques use polynomial chaos (PC) expansions to construct the model proxy, but they differ in their approach to determining the expansions’ coefficients; the fourth technique uses Gaussian Process Regression (GPR). An integral plume model for simulating the Deepwater Horizon oil-gas blowout provides examples for illustrating the different techniques. A Monte Carlo ensemble of 50,000 model simulations is used for gauging the performance of the differen.....
JournalJournal of Geophysical Research: Oceans
Sustainable Development Goals (SDG)14.A
Best Practice TypeManual (incl. handbook, guide, cookbook etc)
DOI Original10.1002/ 2015JC011366
CitationIskandarani, M.; Wang, S.; Srinivasan, A.; Carlisle Thacker, W. ; Winokur, J. and Knio, O.M. (2016) An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations, J. Geophyshysical Research: Oceans, 121, pp.2789–2808 DOI:10.1002/2015JC011366.
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