Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise
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具有过程噪声的线性系统的非侵入式全局灵敏度分析

DOI:
10.1115/1.4041622
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发表时间:
2019
影响因子:
2
通讯作者:
Singh, Tarunraj
Singh, Tarunraj
中科院分区:
工程技术4区
文献类型:
--
作者:
Nandi, Souransu;Singh, Tarunraj

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本文研究了具有时不变模型参数不确定性和随机输入驱动的线性系统的全局灵敏度分析。Sobol指数的演变的均值和方差估计的状态被用来评估的影响的时不变的不确定性模型参数和统计的随机输入的输出的不确定性。两个基准问题的数值结果有助于说明,这是可以想象的参数,这是不那么显着的不确定性的均值,可以是非常显着的方差的不确定性。以拉格朗日插值多项式(LIPs)为多项式混沌(PC)基,采用PC方法综合随机系统的代理概率模型。Sobol指数直接从PC系数中计算。虽然这一概念并不新,随机搭配为基础的PC和侵入PC的一种新的解释,它们被证明是代表相同的概率模型时,所考虑的系统是线性的。这个结果现在允许将线性模型视为黑盒来开发侵入式PC代理。
The focus of this paper is on the global sensitivity analysis (GSA) of linear systems with time-invariant model parameter uncertainties and driven by stochastic inputs. The Sobol' indices of the evolving mean and variance estimates of states are used to assess the impact of the time-invariant uncertain model parameters and the statistics of the stochastic input on the uncertainty of the output. Numerical results on two benchmark problems help illustrate that it is conceivable that parameters, which are not so significant in contributing to the uncertainty of the mean, can be extremely significant in contributing to the uncertainty of the variances. The paper uses a polynomial chaos (PC) approach to synthesize a surrogate probabilistic model of the stochastic system after using Lagrange interpolation polynomials (LIPs) as PC bases. The Sobol' indices are then directly evaluated from the PC coefficients. Although this concept is not new, a novel interpretation of stochastic collocation-based PC and intrusive PC is presented where they are shown to represent identical probabilistic models when the system under consideration is linear. This result now permits treating linear models as black boxes to develop intrusive PC surrogates.
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