An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations

Mohamed Iskandarani, Shitao Wang, Ashwanth Srinivasan, W. Carlisle Thacker, Justin Winokur, Omar M. Knio
2016 Journal of Geophysical Research - Oceans  
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
more » ... an 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 different proxies. The examples illustrate how regressionbased techniques can outperform projection-based techniques when the model output is noisy. They also demonstrate that robust uncertainty analysis can be performed at a fraction of the cost of the Monte Carlo calculation.
doi:10.1002/2015jc011366 fatcat:lsypgtfyungc3nfre4veowvjd4