Data coverage assessment on neural network based digital twins for autonomous control system
We report in a recently developed Nearly Autonomous Management and Control (NAMAC) system, neural networks (NNs) are used to develop digital twins for diagnosis (DT-Ds). However, NNs are not usually considered extrapolation models and may result in large errors if they are applied to unseen data outside the training data (uncovered). In this study, we propose a data coverage assessment (DCA) to determine if the NN-based DT-Ds are extrapolated based on their epistemic uncertainty. The uncertainty quantification algorithms and uncertainty thresholds are selected based on the confusion matrix of classifying evaluation data into covered or uncovered data. To demonstrate the adaptability of the proposed framework, we applied it to a basic feedforward neural network and a more advanced recurrent neural network based on a more nonlinear database. Case studies show that the proposed framework can distinguish unseen data for both basic and advanced applications with proper uncertainty quantification algorithms and thresholds.
- Research Organization:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- DE-AC07-05ID14517
- OSTI ID:
- 1905758
- Report Number(s):
- INL/JOU-22-66939-Rev000; TRN: US2311603
- Journal Information:
- Annals of Nuclear Energy, Vol. 182, Issue -; ISSN 0306-4549
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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