Digital twinUS & West
Integrating ML and uncertainty quantification for real-time decisions in digital twins
A narrative review examines how predictive modeling, machine learning, uncertainty quantification and decision-making are combined in engineering digital twins. It surveys decision architectures and methods addressing their limits across the decision loop, with attention to maintaining them during online operation.
Why it matters
It shows that a digital twin's value comes from the coupling of prediction, uncertainty and decision-making that must be sustained in operation, not from the model alone.
Relevant to DT products
- Original headline
- Integrating Machine Learning and Uncertainty Quantification for Real-Time Decision-Making in Digital Twins: A System-Centric Review and Perspective
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