Hybrid and Heterogeneous Models

The problem here is the integration of deep-physics models with the digital world. The goal is technologies for co-designing and analyzing hybrid networked systems with integrated cyber, engineered, and human elements. The challenges center around the divergent abstractions that are used for physical engineered systems, biological systems, human systems, and computation and networking.


Cyber-physical systems are intrinsically heterogeneous. There are two distinct approaches to modeling heterogeneous systems: (1) a grand unified theory (GUT) and (2) an abstract semantics. The former is about developing a modeling language and conceptual framework into which heterogeneous modeling languages and frameworks can be translated. The latter is about developing interfaces between heterogeneous modeling languages that are sufficient for interoperation, but not so rich that the interface language itself becomes a modeling language. A GUT has the advantage of enabling model exchange between tools, but the disadvantage that the semantic richness that is required to be able to encompass all interesting heterogeneous modeling languages makes analysis of models difficult. An abstract semantics has the advantage of enabling composition of domain-specific modeling languages that are themselves sufficiently constrained that analysis is still possible, but the disadvantage that engineers must learn a multiciplicity of modeling languages and must understand how they interact within an abstract semantics.

Multiform Time

Central to cyber-physical systems is the interaction between cyber and physical components. These interactions occur in time. A naive model of time, which assumes all players in a system have access to a common, smoothly advancing measurement of time, is usually not adequate. In the context of CPS, models of time must be able to deal with non-homogeneous measurements of time, where different parts of a system may disagree on the current time of day. Models of time must also be capable of making a semantic distinction between continuously evolving processes and discrete state changes.