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.

翻译：我们用区域敏感度分析(RSA)来探讨对任何层面的模型产出进行敏感度分析的问题。古典RSA计算了与模型投入变化对模型输出空间目标区域的影响有关的敏感度指数。在这项工作中,我们颠倒了这一视角,提议为特定目标输入模型寻找最能以这种输入的变化来解释其发生的区域。如果存在,这个区域可以被视为一种对所研究的模型投入的变化特别敏感的示范行为。我们将这种方法命名为 iRSA(相对于RSA)。基于区域的敏感度指数正式确定为优化问题,并使用专门的数值算法解决。我们利用分析和数字实例,包括环境模型生成时间序列,表明iRSA可以为不同层面的模型产出提供新的敏感度的图形和可解释的描述。