This project will develop new mathematical foundations and computer-based learning theories for generating a wide range of simulated and fully-synthetic datasets that model interdependent communications and energy infrastructures in urban settings. These enhanced datasets and associated data building tools will provide a large-scale test data related to interdependent critical infrastructures (ICIs). New simulated and synthetic data generation tools will enable increasing the resiliency and flexibility of ICIs, improving their security during extreme weather conditions and other threats. This project will involve students from diverse backgrounds in engineering, computer science and psychology, who will be trained on pertinent research approaches related to the challenges of simulated and synthetic data modeling. The objective is to develop models that can accurately reconstruct, simulate, and evaluate a robust theoretical framework of ICI function by leveraging available real-world datasets. It employs a unique combination of learning and social behavioral models to accomplish the envisioned goal. Also included is a quality-feedback loop verification and data management approach to fine-tune the simulated and synthetic data by comparing it against available real-world data over a realistic network with a large-scale simulator that integrates ICIs over an urban setting.