Abstract An alternative to the theory of probability is applied to the problem of assessing the robustness, to uncertainty in model parameters, of the correlation between measurements and computer simulations. The analysis is based on the theory of information-gap uncertainty, which models the clustering of uncertain events in families of nested sets instead of assuming a probability structure. The system investigated is the propagation of a transient impact through a layer of hyper-elastic material. The two sources of non-linearity are (1) the softening of the constitutive law representing the hyper-elastic material and (2) the contact dynamics at the interface between metallic and crushable materials. The robustness of the correlation between test and simulation, to sources of parameter variability, is first studied to identify the parameters of the model that significantly influence the agreement between measurements and predictions. Model updating under non-probabilistic uncertainty is then illustrated, based on two complementary immunity functions: the robustness to uncertainty and the opportunity from uncertainty. Finally an info-gap model is embedded within a probability density function to represent uncertainty in the knowledge of the model's parameters and their correlation structure. Although computationally expensive, it is demonstrated that info-gap reasoning can greatly enhance our understanding of a moderately complex system when the theory of probability cannot be applied due to insufficient information.