Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions
This addresses the need for efficient digital twin solutions in nuclear engineering, though it is incremental as it applies an existing method to a new domain.
The paper tackled the challenge of real-time inference for nuclear digital twins by evaluating Deep Neural Operator (DeepONet) as a surrogate model, showing it outperforms traditional ML methods in accuracy and speed for particle transport problems.
This paper focuses on the feasibility of Deep Neural Operator (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) for nuclear energy systems. Through benchmarking and evaluation, this study showcases the generalizability and computational efficiency of DeepONet in solving a challenging particle transport problem. DeepONet also exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference. However, the application of DeepONet also reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, DeepONet presents a promising and transformative nuclear engineering research and applications tool. Its accurate prediction and computational efficiency capabilities can revolutionize DT systems, advancing nuclear engineering research. This study marks an important step towards harnessing the power of surrogate modeling techniques in critical engineering domains.