About this Research Topic
Recent rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) offer unprecedented potential to optimize and enhance the safety, efficiency, and management of nuclear applications. These transformative technologies are increasingly being integrated into the nuclear sector to address the most pressing challenges, including innovations in the design of advanced reactor materials, fuel cycle optimization, predictive maintenance strategies, and safety assessments. Breakthroughs in generative AI and reinforcement learning have further opened new pathways for optimizing nuclear systems, simulating complex phenomena, and developing safety protocols.
The Special Topic invites contributions that provide new insights at the intersection of AI, ML, and nuclear applications, bridging theory, methodology, and practical implementation. Interdisciplinary works that combine expertise from nuclear engineering, data science, and computational modeling are particularly encouraged. Articles presenting novel algorithms and case studies on AI-driven improvements in reactor systems are highly sought. Contributions exploring the role of AI and ML in waste management and radiation safety are welcome. We invite submissions focusing on AI-enhanced digital twins for nuclear reactors, explainable AI techniques, and AI applications in fusion energy systems. By featuring studies on these topics, we aim to highlight how AI and ML tools can enhance performance and operational efficiency across nuclear facilities.
Manuscripts should address innovative solutions or novel use cases, with a focus on the following themes:
• AI and ML methods for design, testing, and optimization of nuclear materials.
• Predictive maintenance models and anomaly detection in nuclear systems.
• Machine learning for safety, risk analysis, and reliability assessments in nuclear facilities.
• AI-driven solutions for fuel cycle management, radiation safety, and waste minimization.
• Digital twins and cyber-physical systems in nuclear environments.
• Generative AI and Large Language Models for advancing nuclear research.
• Reinforcement Learning for adaptive system optimization.
• Explainable AI and ethical AI methods in the nuclear sector.
• AI innovations in fusion energy research and technology.
We welcome original research articles, case studies, comprehensive reviews, and perspective studies. Submissions should advance the state of knowledge, present clear methodologies, and align with the overarching goal of fostering innovation in the nuclear domain through AI and ML-driven solutions.
The Special Topic invites contributions that provide new insights at the intersection of AI, ML, and nuclear applications, bridging theory, methodology, and practical implementation. Interdisciplinary works that combine expertise from nuclear engineering, data science, and computational modeling are particularly encouraged. Articles presenting novel algorithms and case studies on AI-driven improvements in reactor systems are highly sought. Contributions exploring the role of AI and ML in waste management and radiation safety are welcome. We invite submissions focusing on AI-enhanced digital twins for nuclear reactors, explainable AI techniques, and AI applications in fusion energy systems. By featuring studies on these topics, we aim to highlight how AI and ML tools can enhance performance and operational efficiency across nuclear facilities.
Manuscripts should address innovative solutions or novel use cases, with a focus on the following themes:
• AI and ML methods for design, testing, and optimization of nuclear materials.
• Predictive maintenance models and anomaly detection in nuclear systems.
• Machine learning for safety, risk analysis, and reliability assessments in nuclear facilities.
• AI-driven solutions for fuel cycle management, radiation safety, and waste minimization.
• Digital twins and cyber-physical systems in nuclear environments.
• Generative AI and Large Language Models for advancing nuclear research.
• Reinforcement Learning for adaptive system optimization.
• Explainable AI and ethical AI methods in the nuclear sector.
• AI innovations in fusion energy research and technology.
We welcome original research articles, case studies, comprehensive reviews, and perspective studies. Submissions should advance the state of knowledge, present clear methodologies, and align with the overarching goal of fostering innovation in the nuclear domain through AI and ML-driven solutions.
Keywords: Digital twins, Machine Learning, Nuclear Applications, nuclear materials, Generative AI
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
