TY - JOUR

T1 - An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations

AU - Iskandarani, Mohamed

AU - Wang, Shitao

AU - Srinivasan, Ashwanth

AU - Carlisle Thacker, W.

AU - Winokur, Justin

AU - Knio, Omar

N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was made possible in part by a grant from BP/The Gulf of Mexico Research Initiative to the CARTHE and DEEP-C Consortia and by the Office of Naval Research, award N00014-101-0498. J. Winokur and O. M. Knio were also supported in part by the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research, under award DE-SC0008789.
This research was conducted in collaboration with and using the resources of the University of Miami Center for Computational Science. The source code for the model used in this study, TAMOC, is freely available at https://github.com/socolofs/tamoc.
The data and input files necessary to reproduce the experiments are
available from the authors upon request (swang@rsmas.miami.edu).
The data are archived at https://github.com/Shitao/A-comparison-of-uncertainty-quantification-techniques-using-integral-plume-model.

PY - 2016/4/22

Y1 - 2016/4/22

N2 - 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 different proxies. The examples illustrate how regression-based 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.

AB - 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 different proxies. The examples illustrate how regression-based 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.

UR - http://hdl.handle.net/10754/611769

UR - http://doi.wiley.com/10.1002/2015JC011366

UR - http://www.scopus.com/inward/record.url?scp=84968906590&partnerID=8YFLogxK

U2 - 10.1002/2015JC011366

DO - 10.1002/2015JC011366

M3 - Article

SN - 2169-9275

VL - 121

SP - 2789

EP - 2808

JO - Journal of Geophysical Research: Oceans

JF - Journal of Geophysical Research: Oceans

IS - 4

ER -