Special Issue "Machine Learning in Advanced Nuclear Engineering and Design"
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 307
Interests: nuclear engineering; reactor physics; machine Learning
Interests: mechanical engineering; nuclear fuel and materials; nuclear safety
Digital twin and the machine learning algorithms behind it has been rapidly investigated and developed in a wide range of industrial applications. However, applying machine learning algorithms in the nuclear engineering has many challenges that may obstruct the deployment of digital twin system in nuclear power plants, the operating which heavily rely on massive expertise experience. To alleviate these challenges, advance modelling and simulation (M&S) methodologies have been proposed and implemented to produce powerful toolkits or software with high-fidelity and reliable M&S capability. These advanced tools for nuclear engineering analysis and design are also able to offer excellent testbed for machine learning algorithms. On the other hand, machine learning algorithms can help to save computation memory and time as compared to high-fidelity M&S toolkits. The aim of this Special Issue is to show off new advances in the application of machine learning and advanced M&S methodologies in nuclear engineering. Both original research and review articles are welcomed.
Dr. Jiankai Yu
Dr. Wei Li
Dr. Xianping Zhong
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- machine learning
- digital twin
- nuclear reactor design and analysis
- advanced modeling and simulation
- nuclear power plants
- advanced nuclear facilities
- code development and verification
- experimental validation