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| 1.Poster Title | Parametric Uncertainty Quantification and Dimensionality Reduction for ALM at FLUXNET Sites |
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| 2.Authors | |
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| 3.Group | Land |
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| 4.Experiment |
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| 5.Poster Category | Early Result |
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| 6.Submission Type | poster |
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| 7.Poster Link | Sargsyan_LandUQ_poster_Nov2016.pdf |
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| 8.Lightning Talk Slide | |
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| View file |
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| name | Sargsyan_LandUQ_poster_Nov2016.pdf |
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| page | 2016-11-09 ACME Fall Meeting Posters |
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| height | 400 |
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Abstract
In this poster, we will present the most recent results of applying multisite parametric uncertainty quantification (UQ) workflow for dimensionality reduction of ACME Land Model followed by targeted, site-specific, low-dimensional accurate surrogate model construction. Surrogate modeling is the key ingredient of the presented work, as it presents a reasonable approximation of input-output maps, as well as provides efficient means for uncertainty propagation and global sensitivity analysis (GSA), otherwise called variance-based decomposition. Specifically, we develop polynomial chaos (PC) surrogates using Bayesian inference. However, the PC surrogate construction still requires a large ensemble of simulations, especially when the number of parameters is large. Here we apply a new procedure, the weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm, which allows a sparse, high-dimensional PC surrogate with very few model evaluations, also quantifying uncertainties due to lack of enough model simulations.
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The surrogate construction machinery is detailed in /wiki/spaces/LND/pages/73793759 and is intended for general use within ACME. The automated workflow relies on the UQTk, lightweight software toolkit for UQ that is available on www.sandia.gov/uqtoolkit.| View file |
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name | Sargsyan_LandUQ_poster_Nov2016.pdf| page | 2016-11-09 ACME Fall Meeting Posters |
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height | 400