论文标题
反转区域灵敏度分析以揭示敏感模型行为
Inverting Regional Sensitivity Analysis to reveal sensitive model behaviors
论文作者
论文摘要
我们使用区域灵敏度分析（RSA）解决了任何维度模型输出的灵敏度分析问题。经典RSA计算与模型输入变化对模型输出空间目标区域发生的影响有关的灵敏度指数。在这项工作中，我们通过建议找到给定的目标模型输入的区域来颠倒这一观点，其出现的区域可以通过此输入的变化来解释。当存在时，该区域可以看作是一种模型行为，对所研究模型输入的变化特别敏感。我们将此方法命名为IRSA（用于逆RSA）。 IRSA使用基于区域的灵敏度指数形式化为优化问题，并使用专用的数值算法解决。使用分析和数值示例，包括产生环境模型的时间序列，我们表明IRSA可以为各个维度的模型输出提供新的图形和可解释的敏感性表征。
We address the question of sensitivity analysis for model outputs of any dimension using Regional Sensitivity Analysis (RSA). Classical RSA computes sensitivity indices related to the impact of model inputs variations on the occurrence of a target region of the model output space. In this work, we invert this perspective by proposing to find, for a given target model input, the region whose occurrence is best explained by the variations of this input. When it exists, this region can be seen as a model behavior which is particularly sensitive to the variations of the model input under study. We name this method iRSA (for inverse RSA). iRSA is formalized as an optimization problem using region-based sensitivity indices and solved using dedicated numerical algorithms. Using analytical and numerical examples, including an environmental model producing time series, we show that iRSA can provide a new graphical and interpretable characterization of sensitivity for model outputs of various dimensions.
