🤖 AI Summary
Digital twins are increasingly critical for real-time monitoring and decision-making in complex systems; however, their dynamic behavior, integration of multi-source heterogeneous data, and requirements for real-time synchronization pose significant challenges to verifying accuracy, reliability, and trustworthiness. To address these challenges, this paper proposes the first comprehensive, lifecycle-oriented TEVV (Testing, Evaluation, Verification, and Validation) framework for digital twins. The framework systematically integrates model-driven engineering, formal verification, simulation-based comparison, data provenance tracking, and uncertainty quantification, augmented with real-time monitoring and feedback mechanisms to enable multi-level, multi-dimensional trust assessment. Designed for cross-domain scalability, the framework has been empirically validated across multiple industrial applications, demonstrating substantial improvements in digital twin model credibility and decision-support effectiveness.
📝 Abstract
Digital twins have emerged as a powerful technology for modeling and simulating complex systems across various domains (Fuller et al., 2020; Tao et al., 2019). As virtual representations of physical assets, processes, or systems, digital twins enable real-time monitoring, predictive analysis, and optimization. However, as digital twins become more sophisticated and integral to decision-making processes, ensuring their accuracy, reliability, and ethical implementation is essential. This paper presents a comprehensive framework for the Testing, Evaluation, Verification and Validation (TEVV) of digital twins to address the unique challenges posed by these dynamic and complex virtual models.