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Open Access

Deep Learning in Nuclear Industry: A Survey

College of Computer Science, Sichuan University, Chengdu 610065, China
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Abstract

As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy. Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medical treatment, radiation processing, and so on. With the development of artificial intelligence and the proposal of "Industry 4.0", more and more artificial intelligence technologies are introduced into the nuclear industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, deep learning, as an important technology of artificial intelligence, has made amazing progress in theoretical and applied research in the nuclear industry, which vigorously promotes the development of informatization, digitization, and intelligence of the nuclear industry. In this paper, we first simply comb and analyze the intelligent demand scenarios in the whole industrial chain of the nuclear industry. Then, we discuss the data types involved in the nuclear industry chain. After that, we investigate the research status of deep learning in the application fields corresponding to different data types in the nuclear industry. Finally, we discuss the limitation and unique challenges of deep learning in the nuclear industry and the future direction of the intelligent nuclear industry.

Keywords

Artificial Intelligence (AI)research statusnuclear industryDeep Learning (DL)development trend

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Big Data Mining and Analytics
Volume 5 Issue 2,
June 2022
Pages 140-160
DOI: 10.26599/BDMA.2021.9020027
Cite this article:
Tang C, Yu C, Gao Y, et al. Deep Learning in Nuclear Industry: A Survey. Big Data Mining and Analytics, 2022, 5(2): 140-160. https://doi.org/10.26599/BDMA.2021.9020027

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Received: 08 December 2021
Accepted: 15 December 2021
Published: 25 January 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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