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Automatic differentiation (AD) is used to perform a multiple parameter sensitivity analysis for the Los Alamos sea-ice model CICE.
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The identification of the relationship among the robustness to multiple parameter perturbation (Equation 13), the sensitivity distribution, and the parameter solution vectors could provide a basis to assume a potential mechanisms generating robustness.
The aim is to understand system robustness by performing sensitivity analyses using multiple parameter sets that yield reasonable model behaviour, as judged by the full cost function.
In robustness analysis, in addition to single parameter sensitivity analysis, robustness to multiple parameter perturbation is defined.
Another significant limitation consists in their capability to consider the sensitivity of the model with respect to the variations of just one parameter at a time; indeed, a model might display low sensitivity to single parameter variations, while being extremely sensitive to simultaneous multiple parameter changes.
Combining the sensitivity analysis and the robustness analysis to multiple parameter perturbation enables identifying critical reactions.
Although there will not be any issue for parameter sensitivity in our system because of linear events, multiple solutions of parameter space, Js, can occur [13].
Global sensitivity analysis, on the other hand, tries to capture the entire parameter space all at once, allowing multiple parameter values to be explored simultaneously.
By using multiple sets of in silico data of varying quality, we illustrate how error in experimental results is propagated to uncertainty in parameter sensitivity.
Additional file 1: Parameter Sensitivity.
(2) S parameter sensitivity: S parameter sensitivity can be obtained by converting its Y parameter sensitivity.
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