The property and set of mechanisms that allow distinct mathematical or computational models to exchange data and operate together coherently across disciplinary boundaries, including syntactic, semantic and runtime compatibility.

Principle

Establish common interfaces, data schemas, units and semantic mappings, or adapter layers, so that coupled models preserve meaning, conserve quantities where required, and manage temporal/spatial coupling consistently.

Demonstration

Coupling an ocean circulation model and an atmospheric model via a shared boundary exchange with agreed units, temporal interpolation, and a mediator that enforces mass and heat flux consistency to produce integrated climate simulations.

Misapplication

Connecting models by simply piping outputs to inputs without unit harmonization, coordinate mapping, or error handling, causing misinterpretation of exchanged fields or unstable coupled runs.

Consequence

When implemented correctly, interoperability enables multi-physics simulation, modular reuse of components, comparative experiments and emergent system-level predictions that individual models cannot provide alone.

Reversal

The opposite is model isolation, where models are run independently and results are post-processed and compared; isolation avoids coupling complexity but misses realtime interactions and feedbacks.

Boundary

Covers mechanisms for exchange and coordination, but does not guarantee model correctness, numerical stability of the coupled system, or semantic completeness beyond the negotiated interfaces.

Semantic Tension

Tension exists between lightweight syntactic interoperability (file formats, APIs) and deep semantic interoperability (shared ontologies, conserved quantities); both are needed but harder to achieve together.

Synthesis

Model interoperability is the combination of interface standards, semantic agreements and runtime adapters that let heterogeneous models exchange meaningfully consistent data and operate jointly to represent coupled systems.