A Multi-Viewpoint Modeling Framework for Digital Twin Integration and Reuse with LLM-Assisted Compatibility Analysis
2026-08-24 • Software Engineering
Software Engineering
AI summaryⓘ
The authors propose a new framework to help combine different computer models in Digital Twin systems, which are virtual copies of real-world systems. Their method organizes information from multiple perspectives and uses smart tools, including AI, to spot potential problems before building the final system. This approach aims to make it easier and clearer to reuse existing models without integration issues, as shown in their expert review and case study on environmental modeling.
Digital TwinModel IntegrationReference Model of Open Distributed Processing (RM-ODP)MetadataModel MetamodelMismatch DetectionLarge Language Models (LLM)Semantic CompatibilityCross-view ConsistencyEnvironmental Modeling
Authors
Nafiseh Soveizi, Milan Kopp, Parinaz Rashidi, Qing Shan, Geerten M. Hengeveld, Ioannis N. Athanasiadis, Zhiming Zhao
Abstract
Digital Twin (DT) ecosystems integrate heterogeneous computational models to represent complex systems under evolving, purpose-specific objectives. Systematic reuse of existing high-quality models and datasets is essential for scalable DT development, yet is constrained by heterogeneity in semantic intent, data structures, behavioral interfaces, and execution environments. As a result, integration becomes a cross-model, cross-view consistency problem that is hard to predict, quantify, and compare across design choices. Existing standards and integration platforms address these concerns separately, offering limited support for structured, purpose-aware compatibility assessment and early feasibility analysis when models are reused under new DT objectives. This paper introduces a multi-viewpoint integration modeling framework grounded in the Reference Model of Open Distributed Processing (RM-ODP). The framework structures integration-relevant knowledge across domain, information, computational, engineering, and technology viewpoints, representing cross-view dependencies as explicit, machine-actionable metadata. It comprises (i) a viewpoint-structured Model Metamodel for systematic model description and discovery, and (ii) a pattern-aware Mismatch Detector that operationalizes cross-view compatibility constraints via integration patterns, combining deterministic rule generation with Large Language Model (LLM)-assisted reasoning. This enables systematic identification of semantic, informational, and runtime inconsistencies and supports reasoning about integration feasibility and effort before implementation. Expert validation and an environmental modeling case study show that the approach enables structured compatibility reasoning, improves transparency of integration assumptions, strengthens cross-view interoperability, and supports scalable reuse in heterogeneous DT ecosystems.