Advances in digital twins and AI/ML for condition monitoring in nuclear applications

Digital Twins (DTs) are emerging as powerful tools to enhance monitoring, maintenance, and safety assurance in nuclear power systems. This review synthesizes recent advances in the integration of DTs with artificial intelligence (AI) and machine learning (ML), emphasizing their application to condition monitoring, inservice testing, and inservice inspection. Case studies illustrate how DT frameworks, ranging from anomaly detection and fault classification to virtual sensing, can improve detection of early degradation, quantify severity, and extend observability into regions inaccessible to physical instrumentation. Collectively, these approaches demonstrate the potential of DTs to shift nuclear safety practices from periodic, schedule-based testing and inspection toward predictive and risk-informed strategies. The review also examines regulatory considerations, highlighting the challenges of qualifying AI/ML-enabled DTs.