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Alen Alexanderian Justin Winokur Ihab Sraj Ashwanth Srinivasan Mohamed Iskandarani William C. Thacker Omar M. Knio 《Computational Geosciences》2012,16(3):757-778
Polynomial chaos (PC) expansions are used to propagate parametric uncertainties in ocean global circulation model. The computations
focus on short-time, high-resolution simulations of the Gulf of Mexico, using the hybrid coordinate ocean model, with wind
stresses corresponding to hurricane Ivan. A sparse quadrature approach is used to determine the PC coefficients which provides
a detailed representation of the stochastic model response. The quality of the PC representation is first examined through
a systematic refinement of the number of resolution levels. The PC representation of the stochastic model response is then
utilized to compute distributions of quantities of interest (QoIs) and to analyze the local and global sensitivity of these
QoIs to uncertain parameters. Conclusions are finally drawn regarding limitations of local perturbations and variance-based
assessment and concerning potential application of the present methodology to inverse problems and to uncertainty management. 相似文献
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A priori testing of sparse adaptive polynomial chaos expansions using an ocean general circulation model database 总被引:1,自引:0,他引:1
Justin Winokur Patrick Conrad Ihab Sraj Omar Knio Ashwanth Srinivasan W. Carlisle Thacker Youssef Marzouk Mohamed Iskandarani 《Computational Geosciences》2013,17(6):899-911
This work explores the implementation of an adaptive strategy to design sparse ensembles of oceanic simulations suitable for constructing polynomial chaos surrogates. We use a recently developed pseudo-spectral algorithm that is based on a direct application of the Smolyak sparse grid formula and that allows the use of arbitrary admissible sparse grids. The adaptive algorithm is tested using an existing simulation database of the oceanic response to Hurricane Ivan in the Gulf of Mexico. The a priori tests demonstrate that sparse and adaptive pseudo-spectral constructions lead to substantial savings over isotropic sparse sampling in the present setting. 相似文献
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Ihab Sraj Kyle T. Mandli Omar M. Knio Clint N. Dawson Ibrahim Hoteit 《Ocean Dynamics》2017,67(12):1535-1551
An efficient method for inferring Manning’s n coefficients using water surface elevation data was presented in Sraj et al. (Ocean Modell 83:82–97 2014a) focusing on a test case based on data collected during the Tōhoku earthquake and tsunami. Polynomial chaos (PC) expansions were used to build an inexpensive surrogate for the numerical model GeoClaw, which were then used to perform a sensitivity analysis in addition to the inversion. In this paper, a new analysis is performed with the goal of inferring the fault slip distribution of the Tōhoku earthquake using a similar problem setup. The same approach to constructing the PC surrogate did not lead to a converging expansion; however, an alternative approach based on basis pursuit denoising was found to be suitable. Our result shows that the fault slip distribution can be inferred using water surface elevation data whereas the inferred values minimize the error between observations and the numerical model. The numerical approach and the resulting inversion are presented in this work. 相似文献
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