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

Iskandarani, Mohamed; Wang, Shitao; Srinivasan, Ashwanth; Carlisle Thacker, W.; Winokur, Justin; Knio, Omar M.

doi:10.1002/2015JC011366

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

Journal Article · Fri Apr 22 00:00:00 EDT 2016 · Journal of Geophysical Research. Oceans

DOI:https://doi.org/10.1002/2015JC011366· OSTI ID:1402364

Iskandarani, Mohamed ^[1]; Wang, Shitao ^[1]; Srinivasan, Ashwanth ^[2]; Carlisle Thacker, W.; Winokur, Justin ^[3]; Knio, Omar M. ^[4]

Rosenstiel School of Marine and Atmospheric Science University of Miami Miami Florida USA
Tendral LLC Miami Florida USA
Sandia National Laboratories Albuquerque New Mexico USA
Department of Mechanical Engineering and Material Science, Duke University Durham North Carolina USA, Division of Computer, Electrical and Mathematical Science and Engineering, King Abdullah University of Science and Technology Thuwal, Saudi Arabia

Abstract

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.

View Accepted Manuscript (Publisher)

Sponsoring Organization:: USDOE

OSTI ID:: 1402364

Journal Information:: Journal of Geophysical Research. Oceans, Journal Name: Journal of Geophysical Research. Oceans Journal Issue: 4 Vol. 121; ISSN 2169-9275

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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