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Simplex-stochastic collocation method with improved scalability
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DOI：10.1016/j.jcp.2015.12.034.png)
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Abstract
En 中文
The Simplex-Stochastic Collocation (SSC) method is a robust tool used to propagate uncertain input distributions through a computer code. However, it becomes prohibitively expensive for problems with dimensions higher than 5. The main purpose of this paper is to identify bottlenecks, and to improve upon this bad scalability. In order to do so, we propose an alternative interpolation stencil technique based upon the Set-Covering problem, and we integrate the SSC method in the High-Dimensional Model-Reduction framework. In addition, we address the issue of ill-conditioned sample matrices, and we present an analytical map to facilitate uniformly-distributed simplex sampling. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Simplex-stochastic collocation method
Uncertainty quantification
Surrogate model
High-dimensional model reduction techniques
Uniform simplex sampling
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