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.

翻译：本文聚焦于深度神经算子（DeepONet）作为稳健替代建模方法在核能系统数字孪生（DT）中的可行性。通过基准测试与评估，本研究展示了DeepONet在求解具有挑战性的粒子输运问题时的通用性与计算效率。DeepONet还展现出卓越的预测精度与速度，优于传统机器学习方法，使其成为适用于数字孪生实时推断的理想算法。然而，DeepONet的应用也揭示了与最优传感器布置和模型评估相关的挑战——这些是实际部署中的关键环节。解决这些挑战将进一步增强该方法的实用性和可靠性。总体而言，DeepONet为核工程研究与应用提供了一种前景广阔且具有变革性的工具。其精准预测与计算效率可彻底改变数字孪生系统，推动核工程研究发展。本研究标志着在关键工程领域发挥替代建模技术潜力的重要一步。