AI-driven real-time diagnostics and self-correcting control schemes for next-generation nuclear energy systems

The advancement of Next-Generation Nuclear Energy Systems (NGNES) requires intelligent, adaptive control mechanisms to enhance operational reliability, safety, and efficiency. Artificial intelligence (AI)-driven real-time diagnostics and self-correcting control schemes are emerging as transformative solutions in nuclear energy infrastructure. These systems integrate machine learning, predictive analytics, and automation to continuously monitor reactor performance, detect anomalies, and autonomously adjust control parameters in response to dynamic operational conditions. This review examines the application of AI-driven models in fault detection, predictive maintenance, and automated response mechanisms within advanced nuclear power plants. It explores key methodologies such as deep learning-based anomaly detection, reinforcement learning for optimal reactor control, and digital twin simulations for predictive diagnostics. By leveraging these AI technologies, nuclear energy systems can improve safety margins, reduce downtime, and optimize energy output, aligning with U.S. Department of Energy (DOE) priorities in nuclear modernization and energy security. However, challenges remain in AI model interpretability, regulatory compliance, cybersecurity risks, and data integration with legacy nuclear infrastructure. Future research should focus on enhancing the robustness of AI models, integrating real-time sensor fusion techniques, and developing standardized frameworks for AI-driven automation in nuclear power operations. By synthesizing recent advancements, this paper provides a comprehensive analysis of the role of AI in real-time diagnostics and self-correcting control schemes, offering insights into how intelligent automation can revolutionize the next generation of nuclear energy systems.