Digital Twins are evolving from passive virtual representations into closed-loop systems that continuously synchronize with physical assets and drive real-time decisions. Yet predictive modeling, machine learning (ML), uncertainty quantification (UQ), and decision-making have been studied as separate capabilities, leaving limited understanding of their integration and maintenance during online operation. This narrative review, scoped by the functional requirements of real-time decision-making rather than by Digital Twin terminology alone, examines how such decisions are generated and sustained in engineering Digital Twins. We first examine foundational decision-making architectures and representative engineering applications, followed by methods that address their limitations across the decision loop.
Foundational Architectures and Applications
We identify optimization-based, policy-based, and knowledge-based approaches as the major pathways from virtual representation to action.
From Virtual Representation to Action
- Optimization-based approaches
- Policy-based approaches
- Knowledge-based approaches
ML in Decision-Making
Across them, ML plays three roles in decision-making:
- Enabling efficient surrogate modeling
- Transforming heterogeneous physical feedback into decision-relevant information
- Accelerating or directly generating decisions
Uncertainty Quantification and Adaptation
UQ supports uncertainty-aware decisions and model adaptation, while continual validation, drift detection, and adaptation help maintain reliable operation as the physical system evolves.
Maintaining Reliable Operation
Together, these findings underscore the need for a shift from a model-centric to a system-centric perspective on Digital Twins, where its performance depends on the interactions with sensing, state estimation, computation, decision-making, and adaptation rather than on their standalone performance.
Conclusion and Future Directions
Therefore, future research should shift from isolated algorithm development toward system-level co-design, scalable decision-making, and approaches to design, and data-efficient continual adaptation and validation for AI-enabled autonomous systems.