Sensitivity Analysis

The constitutive models discussed thus far have not explicitly accounted for the variability in the outputs attributable to the uncertainties in the input model parameters. These uncertainties stem from the errors inherent in the experimental datasets and the employed models, causing the deterministic model parameters to become random variables and models to render stochastic. Sensitivity analysis can provide valuable insights into the effect of input parameters on the model outputs.

Sensitivity analysis is defined as the study of how uncertainty in the output of a model can be apportioned to different sources of uncertainty in the model input. One of the primary goals of SA is factor prioritization, aiming to quantify the contribution of the model inputs to the uncertainty in the model outputs and identify the most influential factors. Parameters exerting minimal influence on the model output can then be assigned reasonable deterministic values, which marks another goal of SA, known as factor fixing. To achieve these objectives, performing both local and global sensitivity analyses is indispensable.