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Global sensitivity analysis of computer models with functional inputs

机译:具有功能输入的计算机模型的全局敏感性分析

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Global sensitivity analysis is used to quantify the influence of uncertain model inputs on the response variability of a numerical model. The common quantitative methods are appropriate with computei codes having scalar model inputs. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Snbol's indices, when some model inputs are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary metamodeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked generalized linear models (GLMs) or generalized additive models (CAMs}. The "mean model" allows to estimate the sensitivity indices of each scalar model inputs, while the "dispersion model" allows to derive the total sensitivity index of the functional model inputs. The proposed approach is compared to some classical sensitivity analysis methodologies on an analytical function, lastly, the new methodology is applied to an industrial computer code that simulates the nuclear fuel irradiation.
机译:全局灵敏度分析用于量化不确定模型输入对数值模型响应变异性的影响。常见的定量方法适用于具有标量模型输入的计算代码。本文旨在说明在某些模型输入具有功能(例如随机过程或随机空间场)的情况下,基于所谓的Snbol指数的不同基于方差的敏感性分析技术。在这项工作中,我们专注于大型cpu时间计算机代码,在执行敏感性分析之前,这些代码需要初步的元建模步骤。我们建议使用联合建模方法,即使用两个相互关联的广义线性模型(GLM)或广义加性模型(CAMs)同时对代码输出的均值和离散进行建模。“均值模型”可以估算灵敏度标量模型输入的每个指标的指数,而“色散模型”允许导出功能模型输入的总灵敏度指数,将该方法与一些经典的灵敏度分析方法在解析函数上进行比较,最后,将该新方法应用于模拟核燃料辐射的工业计算机代码。

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