Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction. (arXiv:2211.13157v1 [stat.AP])

Computationally efficient and trustworthy machine learning algorithms are
necessary for Digital Twin (DT) framework development. Generally speaking,
DT-enabling technologies consist of five major components: (i) Machine learning
(ML)-driven prediction algorithm, (ii) Temporal synchronization between physics
and digital assets utilizing advanced sensors/instrumentation, (iii)
uncertainty propagation, and (iv) DT operational framework. Unfortunately,
there is still a significant gap in developing those components for nuclear
plant operation. In order to address this gap, this study specifically focuses
on the "ML-driven prediction algorithms" as …

arxiv deep learning deep learning framework development digital digital twin framework physics power prediction stage state