Special Issue "Machine Learning in Advanced Nuclear Engineering and Design"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Physics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 307

Special Issue Editors

Department of Nuclear Engineering, University of Tennessee Knoxville, Knoxville, TN 37996, USA
Interests: nuclear engineering; reactor physics; machine Learning
School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: mechanical engineering; nuclear fuel and materials; nuclear safety
Department of Mechanic Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA
Interests: nuclear engineering; reactor thermal hydraulics; machine learning

Special Issue Information

Dear Colleagues,

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
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

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
  • high-fidelity
  • nuclear power plants
  • advanced nuclear facilities
  • code development and verification
  • experimental validation

Published Papers

There is no accepted submissions to this special issue at this moment.
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