Digital Twins in Fusion Energy Research: Current State and Future Directions

This review paper explores the pivotal role of digital twin (DT) technology in advancing nuclear fusion research and its potential to address key challenges in achieving fusion energy as a sustainable power source. Whilst DTs have been developed to tackle complex issues in data analysis, real-time control, optimisation, and simulation, existing efforts are largely fragmented and constrained to isolated applications due to the experimental and evolving nature of fusion reactors. To bridge this gap, there is a pressing need for an integrated, end-to-end DT framework that can unify these capabilities, enabling a more holistic approach to reactor design and operation. This paper systematically reviews the current state of DT development, focusing on its application to critical components such as the divertor, breeder blanket, and magnets, which serve as ideal candidates for modular DT instances. By leveraging high-performance computing and vast datasets generated by fusion experiments worldwide, we identify opportunities to create scalable, data-driven surrogate models that can continuously refine live DT systems. The findings highlight the potential for centralised storage of the data and models to accelerate simulation-based testing (in silico) and optimisation of reactor components, ultimately contributing to the faster realisation of commercially viable fusion reactors. This integration of DT technology promises to significantly reduce the need for costly, iterative physical testing, paving an efficient pathway to achieving fusion as a reliable energy source.