From Local Accuracy to Global Forecasting: Expert Selection for Multistep Multivariable Tokamak Fusion Dynamics

Amid growing concerns over the global energy crisis and climate change, fusion energy has emerged as a promising route toward sustainable power, while artificial intelligence is increasingly being used to model and control tokamak plasmas. Yet its dynamics are high-dimensional, strongly coupled, and only partially observed, while practical forecasting requires multistep recursive prediction across heterogeneous physical variables. Existing deep learning predictors are difficult to achieve consistent and accurate predictions across all physical variables and time steps using a single model. Moreover, when using predictions as recursive inputs for future models, local accuracy may not necessarily determine the performance of multi-step predictions. Based on the above discovery, we propose that different models should be adaptively used for different time steps and physical variables, and systematically compared the four Mixture-of-Experts (MoE) frameworks in this work, including the behavior-cloning baseline (Baseline-MoE), DAgger-style MoE (DaD-MoE), reinforcement-learning-based MoE (PPO-MoE), and full-information rollout-aware MoE (FIRE) over a frozen bank of heterogeneous physical predictors. We first evaluate the proposed MoE framework through simulation and further extend them to real-world device measurements. The results show that PPO-MoE achieves the lowest complete-rollout error on simulated MAST-U vertical-displacement events, reducing it by $6.38\%$ relative to Baseline-MoE. On experimental measurement from the real-world MAST device, FIRE reduces complete-rollout error by $12.29\%$ relative to the best fixed-expert reference and by $7.81\%$ relative to Baseline-MoE. These results demonstrate that adaptive expert routing can improve recursive multivariable plasma forecasting and offer a new perspective on forecasting in fusion-energy systems.Code is available at https://anonymous.4open.science/r/test0924-16A6.