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

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 Topic 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