Digital Twin: Graph Formulations for Managing Complexity and Uncertainty

This paper articulates the importance of graphical models and graph-based methods as fundamental enablers of Digital Twins. Graph-based representations are well known to be suited for describing complex systems where the connections between entities are as important as the entities themselves. The interconnections within and across data, models, and decisions are central to a Digital Twin’s value. Not only does a graph emphasize the scalable representation of such interrelationships, it also provides a natural mathematical setting for addressing uncertainty and complexity—arguably the two biggest barriers to scalable deployment and adoption of Digital Twins. We discuss how recent advances in theory and algorithms for large-scale knowledge graphs and graphical models can be combined in a multi-layered formulation to provide a powerful foundation for achieving scalable Digital Twins for complex systems.