Network Reliability

Network reliability research focuses on assessing and enhancing the robustness of interconnected systems against failures, aiming to ensure continued functionality under various stress conditions. Current efforts concentrate on developing efficient algorithms, such as improved implicit enumeration methods and Monte Carlo sampling techniques often coupled with machine learning models like random forests and graph neural networks, to handle the computational complexity of large-scale networks. These advancements are crucial for optimizing diverse applications, including power grid management (e.g., efficient electric vehicle charging), transportation infrastructure resilience (e.g., seismic bridge network analysis), and improving the performance of communication networks. The ultimate goal is to develop predictive and adaptive methods that enable proactive management and mitigation of potential disruptions.