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As a form of clean energy, nuclear energy has unique advantages compared to other energy sources in the present era, where low-carbon policies are being widely advocated. The exponential growth of artificial intelligence (AI) technology in recent decades has resulted in new opportunities and challenges in terms of improving the safety and economics of nuclear reactors. This study briefly introduces modern AI algorithms such as machine learning, deep learning, and evolutionary computing. Furthermore, several studies on the use of AI techniques for nuclear reactor design optimization as well as operation and maintenance (O (2) black-box dilemma: methods such as deep learning have poor interpretability. Finally, this study proposes two directions for the future fusion of AI and nuclear reactor technologies: (1) better integration of domain knowledge with data-driven approaches to reduce the high demand for data and improve the model performance and robustness; (2) promoting the use of explainable artificial intelligence (XAI) technologies to enhance the transparency and reliability of the model. In addition, causal learning warrants further attention owing to its inherent ability to solve out-of-distribution generalization (OODG) problems.
Huang et al. (Tue,) studied this question.