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DOE OSTI · 22701623

Modified GMDH-NN algorithm and its application for global sensitivity analysis

Song, Shufang·Wang, Lu
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Abstract

Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages. - Highlights: • The GMDH-NN is improved to construct the explicit polynomial model of optimal complexity by self-organization. • The paper aims at combining improved GMDH-NN with HDMR expansions and using it to compute Sobol' indices directly. • The method can be applied in uniform, normal and exponential distribution by using suitable orthogonal polynomials. • Engineering examples, e.g., electronic circuit models can be solved by the presented method.

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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICSALGORITHMSELECTRONIC CIRCUITSSENSITIVITY ANALYSIS

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Song, Shufang, Wang, Lu. 2017-11-01. Modified GMDH-NN algorithm and its application for global sensitivity analysis. https://doi.org/10.1016/j.jcp.2017.07.027

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