Topic Editors

Dr. Dinesh Kumar
Department of Mechanical Engineering, University of Bristol, Bristol, UK
1. Director, MARTIANS Lab (Machine Learning and ARTificial Intelligence for Advancing Nuclear Systems), Missouri University of Science and Technology, Rolla, MO 65409, USA
2. Assistant Professor, Nuclear Engineering and Radiation Science, Missouri University of Science and Technology, Rolla, MO 65409, USA

Intelligent, Explainable and Trustworthy AI for Advanced Nuclear and Sustainable Energy Systems

Abstract submission deadline
closed (13 October 2023)
Manuscript submission deadline
31 January 2024
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Topic Information

Dear Colleagues,

Machine learning (ML) and artificial Intelligence (AI) are increasingly used in nuclear and sustainable energy systems. The United States Nuclear Regulatory Research (NRC) and the Department of Energy (DOE) initiated a significant research effort to determine the feasibility of ML/AI-driven techniques to advance energy systems research. These tools facilitate risk-informed decision-making and streamline high-performing simulations by analyzing vast amounts of data. However, these models must be fair, unbiased, explainable, and overall intelligent in nature to gain confidence in AI's trustworthiness. In order to assure trustworthiness for decision-making, ML/AI techniques need to be audited, accounted for, and easy to understand for the energy systems. Furthermore, the concepts of explainable AI (XAI) and interpretable machine learning (IML) need to be incorporated to understand the reasoning behind the prediction of complex energy systems. This understanding can lead to better maintenance and repair planning and improved system performance for sustainable energy systems. This Special Issue aims to explore potential improvements and current research in ML and AI that are explainable and trustworthiness and incorporate AI risk management for energy systems. Potential authors are encouraged to submit novel ideas, concepts, and results by following the submission guidelines.

Dr. Dinesh Kumar
Dr. Syed Bahauddin Alam
Topic Editors


  • uncertainty quantification
  • surrogate modeling
  • uncertainty aware data-driven algorithms
  • explainable artificial intelligence
  • machine learning risk assessment
  • robust optimization

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
- - 2020 21.8 Days CHF 1200 Submit
2.3 3.7 2008 19.1 Days CHF 1600 Submit
Applied Sciences
2.7 4.5 2011 15.8 Days CHF 2300 Submit
3.2 5.5 2008 15.7 Days CHF 2600 Submit
Journal of Nuclear Engineering
- - 2020 21.7 Days CHF 1000 Submit

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Published Papers

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