Surrogate Models for Uncertainty Propagation and Sensitivity Analysis

Khachik Sargsyan ()
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Khachik Sargsyan: Sandia National Laboratories, Reacting Flow Research Department

Chapter 19 in Handbook of Uncertainty Quantification, 2017, pp 673-698 from Springer

Abstract: Abstract For computationally intensive tasks such as design optimization, global sensitivity analysis, or parameter estimation, a model of interest needs to be evaluated multiple times exploring potential parameter ranges or design conditions. If a single simulation of the computational model is expensive, it is common to employ a precomputed surrogate approximation instead. The construction of an appropriate surrogate does still require a number of training evaluations of the original model. Typically, more function evaluations lead to more accurate surrogates, and therefore a careful accuracy-vs-efficiency tradeoff needs to take place for a given computational task. This chapter specifically focuses on polynomial chaos surrogates that are well suited for forward uncertainty propagation tasks, discusses a few construction mechanisms for such surrogates, and demonstrates the computational gain on select test functions.

Keywords: Bayesian inference; Global sensitivity analysis; Polynomial chaos; Regression; Surrogate modeling (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-12385-1_22

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DOI: 10.1007/978-3-319-12385-1_22

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