SYSEN 5470

Network Science and Applications for Systems Engineering

Course Description

Course information provided by the 2025-2026 Catalog.

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.

Last 4 Terms Offered (None)

Learning Outcomes

• Analyze network data and structure – process and analyze network datasets using relational databases and network analysis libraries to understand system connectivity and topology.
• Visualize and communicate network insights – create publication-quality network visualizations to communicate complex network relationships to stakeholders.
• Measure network centrality and importance – apply centrality measures and network statistics to identify critical nodes, edges, and system vulnerabilities across different network types.
• Model network dynamics and outcomes – develop network models to predict edge and node-wise outcomes, including network permutation tests and statistical validation.
• Optimize network routing and flows – solve routing problems and network optimization challenges for transportation, logistics, and information flow systems.
• Apply network clustering techniques – use clustering algorithms to identify communities, groups, and hierarchical structures within complex networks.
• Use machine learning and statistics for network analysis - compare value-added of new machine learning and AI approaches to network inference against network statistical frameworks.
• Build network analysis codes and procedures to communicate network insights for data-driven decision-making.

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