Constraint Hypergraphs as a Unifying Framework for Digital Twins (2507.05494v1)

Abstract: Digital twins, used to represent physical systems, have been lauded as tools for understanding reality. Complex system behavior is typically captured in domain-specific models crafted by subject experts. Contemporary methods for employing models in a digital twin require prescriptive interfaces, resulting in twins that are difficult to connect, redeploy, and modify. The limited interoperability of these twins has prompted calls for a universal framework enabling observability across model aggregations. Here we show how a new mathematical formalism called a constraint hypergraph serves as such a framework by representing system behavior as the composition of set-based functions. A digital twin is shown to be the second of two coupled systems where both adhere to the same constraint hypergraph, permitting the properties of the first to be observable from the second. Interoperability is given by deconstructing models into a structure enabling autonomous, white-box simulation of system properties. The resulting digital twins can interact immediately with both human and autonomous agents. This is demonstrated in a case study of a microgrid, showing how both measured and simulated data from the aggregated twins can be provided regardless of the operating environment. By connecting models, constraint hypergraphs supply scientists and modelers robust means to capture, communicate, and combine digital twins across all fields of study. We expect this framework to expand the use of digital twins, enriching scientific insights and collaborations by providing a structure for characterizing complex systems.