AI across the nuclear lifecycle: moving from isolated demonstrations to trustworthy decision support requires assessing complete workflows, not just predictive models
This review surveys evidence on artificial intelligence across the nuclear engineering lifecycle—covering nuclear datasets and computational infrastructure, surrogate and physics-informed modeling, digital twins, monitoring and prognostics, optimization and control, and trustworthy AI—reporting that learning-based methods can accelerate high-fidelity calculations, extract information from multivariate measurements, and support decisions in reactor operation, maintenance, waste management, and environmental assessment, while exposing recurring limitations such as scarce abnormal-condition data, differences between simulated and physical systems, uncertain generalization, and incomplete evaluation of downstream decisions, and arguing that progress depends on assessing complete AI-enabled wor