Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions

TL;DR

This paper demonstrates that Deep Neural Operator (DeepONet) can serve as an efficient, accurate surrogate model for real-time digital twin applications in nuclear systems, outperforming traditional methods.

Contribution

It introduces DeepONet as a novel surrogate modeling approach for nuclear digital twins, highlighting its generalizability, speed, and accuracy in complex particle transport problems.

Findings

DeepONet outperforms traditional ML methods in speed and accuracy.

DeepONet demonstrates strong generalizability across problems.

Challenges include sensor placement and model evaluation.

Abstract

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.