AI for Fusion Energy: Accelerating STEP Tokamak Development

At the AI for Science event, Lorenzo Z of the UK Atomic Energy Authority presents work on using AI to accelerate the path to fusion energy, focusing on the STEP spherical tokamak power plant planned to deliver electricity to the grid in the 2040s and backed by £2.5 billion funding to 2030. He explains tokamak fusion, progress toward breakeven, and the need for high fidelity simulation and digital twins that combine messy experimental diagnostics with simulations. He describes three pillars: real time forecasting with multimodal foundation models and FAIR fusion datasets (FAIRMAST); plasma control using simulators such as FreeGSNKE inspired by reinforcement learning advances; and surrogate models that speed up turbulence, edge, and MHD simulations from weeks to hours or milliseconds, enabling design trade offs, uncertainty quantification, active learning to cut data costs, and large compression for simulation storage.

Lorenzo Zanisi is a Lead Data Scientist at the UK Atomic Energy Authority (UKAEA), working on one of the most consequential applications of AI in science today: fusion energy. With a PhD in Astrophysics in which he applied AI to galaxy formation simulations, he now leads a team developing surrogate models for fusion plasmas, with pioneering applications to both experiments and reactor design, already delivering speedups of 100x over conventional approaches.