  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.
