Heterogeneous Model Alignment in Digital Twin

Digital twin (DT) technology integrates heterogeneous data and models, along with semantic technologies to create multi-layered digital representation of physical systems. DTs enable monitoring, simulation, prediction, and optimization to enhance decision making and operational efficiency. A key challenge in multi-layered, model-driven DTs is aligning heterogeneous models across abstraction layers, which can lead to semantic mismatches, inconsistencies, and synchronization issues. Existing methods, relying on static mappings and manual updates, are often inflexible, error-prone, and risk compromising data integrity. To address these limitations, we present a heterogeneous model alignment approach for multi-layered, model-driven DTs. The framework incorporates a flexibility mechanism that allows metamodels to adapt and interconnect seamlessly while maintaining semantic coherence across abstraction layers. It integrates: (i) adaptive conformance mechanisms that link metamodels with evolving models and (ii) a large language model (LLM) validated alignment process that grounds metamodels in domain knowledge, ensuring structural fidelity and conceptual consistency throughout the DT lifecycle. This approach automates semantic correspondences discovery, minimizes manual mapping, and enhances scalability across diverse model types. We illustrate the approach using air quality use case and validate its performance using different test cases from Ontology Alignment Evaluation Initiative (OAEI) tracks.