Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems.

ABSTRACT: This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solvi

SUBMITTER: Kobayashi K 

PROVIDER: S-EPMC10808608 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature