Relation Between Magnetic Geometry and Stellarator Confinement using Data-Driven Methods

Traditionally, stellarator design has been performed using numerical optimization that was informed mostly by physics goals. Recently, several databases of stellarator configurations have been created, using either near-axis expansions [1], coil sets [2], or MHD equilibria [3]. Such data can provide important insights to make informed decisions on the design of the next generation of fusion devices. In this work [4], we assess how databases can be used to inform new designs. Using a large number of confinement metrics such as loss fraction of ions, omnigenity, MHD stability, and the magnetic gradient scale length, we look at the relations between geometry and confinement metrics, as well as the pair-wise distributions between such metrics. Furthermore, we construct surrogate models such as supervised autoencoders to be used in optimization efforts. Finally, we determine Shapley values to assess the relative influence of different geometry coefficients in each metric.



[1] P. Curvo, D. R. Ferreira, R. Jorge 2025. “Using deep learning to design high aspect ratio fusion devices.” Journal of Plasma Physics 91(1), E38

[2] A. Giuliani 2024. “Direct stellarator coil design using global optimization: application to a comprehensive exploration of quasi-axisymmetric devices.” Journal of Plasma Physics 90(3), 905900303

[3] M. Landreman, et al. “How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning.” arXiv:2502.11657

[4] R. Laia, R. Jorge, G. Abreu 2025. “Data-Driven Approach to Model the Influence of Magnetic Geometry in the Confinement of Fusion Devices.” arXiv:2507.03776