Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook
Software Engineering
Summary
The authors suggest a new kind of digital twin called a trans-domain digital twin, which links different types of systems by sharing states, data, and controls more directly and operationally than earlier methods. Their approach involves a detailed architecture to manage coordination, decision-making, and adaptation across diverse systems over different time scales. They also propose ways to check how well this system works, including safety and version management, but emphasize that it still needs thorough real-world testing. This framework could help improve how complex interconnected systems are monitored and controlled.
Authors
Mansoorali Amiri
Abstract
Complex systems comprise heterogeneous domains whose states, uncertainties, risks, and control consequences can cross domain boundaries. Existing cross-domain digital twin approaches broadly focus on comparison, reuse, semantic mapping, standardization, and interoperability, but do not inherently require operational connections among domain states, errors, objectives, constraints, decisions, and controls. This article proposes the trans-domain digital twin as an operational formulation along the continuum of Composite/Federated Digital Twin Systems. This approach connects heterogeneous domain twins through an aligned shared state, explicit coupling of data, models, states, errors, objectives, and controls, heterogeneous temporal coordination, joint decision-making, and feedback-based adaptation. The proposed framework presents a seven-layer conceptual architecture, a trans-domain orchestration core, minimum compliance conditions, a general operational formalism, progressive fast-meso-slow loops, and a single-episode offline training mechanism linked to bounded online adaptation. It also describes conceptual validation and evaluation criteria, a maturity model, a reference deployment architecture, and requirements for runtime safety, provenance, versioning, and model lifecycle management. The framework is conceptually mappable to standards for digital twins, model exchange, distributed simulation, and smart transducers; however, its formal compliance and operational effectiveness must be examined through independent benchmarks, uncertainty quantification, ablation testing, and field validation.