Critical Infrastructure network analysis enabled by simulation metamodeling

This paper presents an application of simulation metamodeling to improve the analysis capabilities within a decision support tool for Critical Infrastructure network evaluation. Simulation metamodeling enables timeliness of analysis, which was not achievable by the original large-scale network simulation due to long set-up times and slow run times. We show through a case study that the behavior of a large-scale simulation for Critical Infrastructure analysis can be effectively captured by Neural Network metamodels and Stochastic Kriging metamodels. Within the case study, metamodeling is integrated into the second step of a two-step analysis process for vulnerability assessment of the network. This consists first of an algorithmic exploration of a power grid network to locate the most susceptible links leading to cascading failures. These links represent the riskiest links in the network and were used by the metamodels to visualize how their failure probabilities affect global network performance measures.