Francesca Campolongo

TL;DR: Existing and new practices for sensitivity analysis of model output are compared and recommendations on which to use are offered to help practitioners choose which techniques to use.

TL;DR: The majority of sensitivity analyses met with in chemistry and physics are local and derivative-based, and local sensitivities are useful for a variety of applications, such as the solution of inverse problems or the analysis of runaway and parametric sensitivity of various types of chemical reactors.

TL;DR: It is demonstrated that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear, and it is shown that available good practices are able to overcome OAT shortcomings and easy to implement.

TL;DR: It is found that the radial design is indeed superior even for the computation of the elementary effects method, opening the door to a sensitivity analysis strategy whereby the analyst can start with a small number of points and then - depending on the results - possibly increase the numeral of points up to compute a fully quantitative measure.

TL;DR: Local, derivative-based sensitivity measures can be efficiently computed by an array of techniques, ranging from automated differentiation ( where the computer program that implements the model is modified so that the sensitivities are computed with a modicum of extra execution time) to direct methods (where the differential equations describing the model are solved directly in terms of species concentrations and their derivatives).