coupled digital-twin modeling

Integrating and enabling interoperability among heterogeneous digital models (DMs) from multiple sources in digital twins (DTs) faces persistent challenges—including the absence of standardized interfaces, high manual adaptation costs, and difficulties in cross-lifecycle model reuse—yet empirical, industry-grounded studies addressing these issues remain scarce. Method: This study conducts the first large-scale, cross-sectoral survey involving domain experts across diverse industries, complemented by expert interviews, thematic coding, and systematic requirement elicitation. Contribution/Results: We identify three fundamental bottlenecks hindering DM integration in DTs and propose semantic-driven interoperability and automated model orchestration as critical technical pathways forward. The findings constitute the first empirically grounded, industry-consensus-based evidence and technology roadmap for standardizing, automating, and semantically enabling DM integration within DT ecosystems.

Digital twins (DTs) suffer from conceptual ambiguity and poor cross-domain reusability due to the absence of a unified definition and standardized reference architecture (RA). To address this, we propose TwinArch—the first domain-agnostic, multi-perspective DT reference architecture. TwinArch innovatively decouples structural and dynamic behavioral views to eliminate semantic ambiguity inherent in monolithic architectures. It adopts the “Views and Beyond” methodology for rigorous architectural modeling and is rigorously validated through three iterative design science research cycles: a systematic literature review, industrial practice feedback, and expert validation involving 20 domain specialists. TwinArch supports DT system design, development, and documentation of legacy systems; its architecture website and reproducibility package are publicly open-sourced. Expert evaluation confirms its completeness and engineering applicability, establishing TwinArch as a plug-and-play architectural foundation for smart manufacturing, smart cities, and other DT-intensive domains.

To address the loss of fidelity in digital twins caused by dynamic physical system evolution—such as maintenance, wear, and human intervention—this paper proposes a model-verification-based continuous validation framework. The framework integrates real-time monitoring with historical data comparison to construct an interpretable validation metric system, incorporates a lightweight anomaly detection mechanism, and introduces a data-driven parameter self-adaptation estimation algorithm for online twin diagnosis and closed-loop model updating. Unlike conventional static calibration methods, our approach enables long-term trustworthiness preservation and autonomous evolution of the digital twin. Evaluated on an industrial quay crane use case, the framework accurately detects system deviations and dynamically refines model parameters, reducing modeling error by 37.2% and improving maintenance response timeliness by 52%. These results demonstrate significant enhancements in the representativeness, robustness, and engineering practicality of digital twins.

Existing digital twin definitions and reference models are overly abstract, leading to a significant disconnect between theoretical concepts and industrial practice. Method: This paper proposes a unified, CPS-oriented digital twin reference model, systematically synthesizing mainstream frameworks to establish a fine-grained architecture with explicit hierarchical structure, well-defined component relationships, and operational interfaces. The model integrates strengths from ISO/IEC 23053 and the Digital Twin Capability Maturity (DTCM) framework. Contribution/Results: It advances conceptual clarity, modeling granularity, and engineering mapping: (1) improves terminology consistency and semantic interpretability; (2) explicitly specifies data, control, and service flows governing cyber-physical interaction; and (3) provides actionable implementation pathways across modeling, integration, and verification phases. Experimental validation in three representative industrial scenarios demonstrates that the model supports digital twin system development, reducing modeling time by approximately 35% and enhancing cross-organizational collaboration efficiency.

This study addresses the challenges of non-standardized, time-consuming, and resource-intensive digital twin development by proposing a tool-supported framework that, for the first time, enables the automatic derivation of purpose-specific digital twins from existing engineering models. By reusing pre-existing structural and behavioral models of physical assets and integrating lightweight customization and configuration, the approach leverages model-based engineering methods and automated generation techniques to rapidly align digital twins with operational objectives. The framework’s feasibility, generality, and engineering practicality are demonstrated through the successful automatic generation of digital twin instances across four heterogeneous use cases.

This work addresses the challenge that existing cross-domain digital twin approaches struggle to effectively coordinate the intrinsic operational relationships among heterogeneous domains in terms of states, errors, objectives, constraints, and control. To overcome this limitation, the paper proposes a novel cross-domain digital twin framework featuring an original seven-layer conceptual architecture and a cross-domain orchestration core. The framework enables deep multi-domain coordination through shared state alignment, explicit coupling modeling, heterogeneous temporal coordination, joint decision-making, and a feedback-driven adaptive mechanism. It incorporates a single offline training phase with bounded online adaptation and integrates model lifecycle management, runtime safety, and provenance tracking. Compatible with mainstream digital twin and simulation standards, the approach is accompanied by validation criteria, a maturity model, and a deployment architecture, thereby establishing a foundation for benchmarking and real-world implementation.

This study addresses the deep integration of modeling and simulation (M&S) with artificial intelligence (AI) within digital twins to enhance their intelligent prediction and autonomous decision-making capabilities. By developing a comprehensive framework that incorporates physical modeling, discrete-event, and hybrid simulation methods alongside AI-driven advanced analytics and predictive modeling, the work elucidates the bidirectional synergy between M&S and AI. It positions the digital twin as a pivotal enabling platform for this convergence, revealing its multifaceted roles across business, development, and operational contexts. The research establishes an integrated theoretical foundation, identifies critical challenges, and outlines future directions to advance more intelligent and cohesive digital twin systems.

This work addresses the fragmentation in current quantum network evaluation across heterogeneous platforms, simulators, and protocols, which lacks a reusable, system-level digital twin solution. To bridge this gap, the study systematically introduces Model-Driven Engineering (MDE) to construct a digital twin framework for quantum networks that supports both design-time assessment and runtime synchronization. It proposes an architectural evolution path transitioning from code-driven integration toward a hub-and-spoke paradigm. A proof-of-concept implementation leverages SysML v2 modeling, a QKD kit, an EMF-based controller, and the SeQUeNCe simulation platform, demonstrating unified modeling of heterogeneous components, evolvable integration, and multi-layer interoperability. This approach establishes an adaptable and verifiable foundational infrastructure for quantum networks.

This work proposes a unified digital twin–based framework to address the heightened complexity in design, operation, and maintenance of smart grids arising from the tight coupling between physical and software components. For the first time, it systematically demonstrates the pivotal role of digital twins across the entire grid lifecycle. By constructing a high-fidelity virtual replica that integrates real-time simulation and automated decision-making, the approach enables safe and efficient validation during the design phase and facilitates dynamic load balancing and intelligent control during operation. The framework not only provides a low-cost, high-safety environment for experimentation and maintenance—significantly enhancing system efficiency and automation—but also establishes a solid theoretical and practical foundation for the engineering application of digital twins in energy systems.