DMCA
Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs (2012)
BibTeX
@MISC{Ruppert12uncertaintyanalysis,
author = {David Ruppert and Christine A. Shoemaker and Yilun Wang and Yingxing Li and Nikolay Bliznyuk},
title = {Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs},
year = {2012}
}
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Abstract
Bayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology. SOARS uses a radial basis function interpolator as a surrogate, also known as an emulator or meta-model, for the logarithm of the posterior density. To prevent wasteful evaluations of the expensive model, the emulator is built only on a high posterior density region (HPDR), which is located by a global optimization algorithm. The set of points in the HPDR where the expensive model is evaluated is determined sequentially by the GRIMA algorithm described in detail in another paper but outlined here. Enhancements of the GRIMA algorithm were introduced to improve efficiency. A case study uses an eight-parameter SWAT2005 (Soil and Water Assessment Tool) model where daily stream flows and phosphorus concentrations are modeled for the Town Brook watershed which is part of the New York City water supply. A Supplemental Material file available online contains additional technical
Keyphrases
uncertainty analysis expensive model multiple output computationally expensive model grima algorithm phosphorus concentration town brook wasteful evaluation bayesian mcmc calibration optimization analysis eight-parameter swat2005 response surface daily stream flow high posterior density region global optimization algorithm case study new york city water supply supplemental material file available online water assessment tool radial basis function interpolator posterior density
