Graph Intelligence for Networked Physical Systems

Many of the systems studied here are naturally relational: base stations interact through interference and topology, farms are organised through sensors, valves, gateways, and terrain, and infrastructure assets influence one another across space and time. Graph intelligence focuses on learning directly from this structure rather than treating it as unstructured data.

This creates a strong foundation for tasks such as greenfield site planning, adaptive sensor placement, capacity estimation, interference-aware control, and distributed resource coordination. The broader objective is to use graph-based learning to build models that are more scalable, more physically meaningful, and better aligned with real networked environments.