Towards Digital Twins for Nuclear Safeguards

Digital twins are increasingly emerging as a transformative tool for nuclear safeguards. Their expanding use reflects major advances in simulation accuracy, sensor integration, and real time data assimilation. By enabling continuous monitoring and predictive modeling, digital twins allow analysts to evaluate system behavior under a wide range of operational scenarios without interrupting facility activities. For safeguards applications, these capabilities translate into several important benefits. Digital twins can significantly reduce inspection costs by optimizing measurement planning, decreasing reliance on physical access, and enabling remote verification pathways. They also enhance anomaly detection through predictive baselining, allowing earlier and more sensitive identification of deviations from expected system behavior. Furthermore, digital twins strengthen the overall robustness of safeguards by supporting standardized evaluation, operator training, and advanced analytics—including machine learning enabled pattern recognition. As the nuclear enterprise modernizes, digital twins have become essential to meeting evolving verification challenges. Their ability to integrate complex system data, reduce costs, and improve detection fidelity underscores their growing importance in safeguarding nuclear materials and supporting the mission of nonproliferation. This panel aims to bring together researchers to discuss advances on digital twins for safeguards and share their views on the future.