Network Science and Applications for Systems Engineering

Catalog description

Modern engineering systems—like supply chains, power grids, and logistics networks—rely on complex, interconnected networks. This course trains Systems Engineers to model, analyze, and optimize these systems using network science, statistics, machine learning, and AI. Students gain hands-on experience with network visualization, clustering, routing optimization, and performance modeling under uncertainty. Case studies include logistics networks, power grids, data centers, and crisis response systems. Participants will learn to identify critical nodes, simulate and optimize flows, and communicate results through interactive visualizations. By course end, students will develop complete network analysis workflows connecting theory to systems engineering practice. Designed for graduate students in technical roles, the course requires 3–4 hours of work per credit hour.