Combining physics-based simulations and experimental data from multiple machines to predict and control tokamak profile evolution

Hominid tokamak scientists and operators combine experimental experience and physical models to guide decision-making in machine design and control. Methods for "data fusion" are beginning to provide practical methodologies to build AI models mimicking this human process: taking advantage of both the generalizability of physical models and the quantitative accuracy of experimental results in a single model. For the task of tokamak plasma profile prediction, a variety of such methodologies are presented: (1) multi-machine learning exploiting non-dimensionalization, (2) providing interpreted context from simulations as additional input to machine learning models, (3) transfer learning from simulation to experimental data, and (4) meta-learning (akin to stacked generalization) by combining physics-based and empirical models on equal footing. It is demonstrated that, for the task of extrapolating plasma profile predictions from low- to high-plasma current DIII-D scenarios, a meta-learned profile-predictor using ASTRA/TGLF physics simulations and data is more accurate than a model built on physics or data alone. Applications of the methodology to the task of commissioning a new reactor such as ITER are discussed.