Session

DescriptionNuclear fusion is increasingly seen as a credible complement to renewable energy, driven today by both public and growing private investments. The leading fusion concepts, tokamaks and stellarators, rely on magnetic confinement. Fusion plasmas exhibit multiscale electromagnetic instabilities, from machine-scale disruptions to microscopic turbulence. The development of these systems thus requires extensive high-performance computing to understand their complex physics and to address the engineering challenges. Fully kinetic models are prohibitively expensive, so hierarchies of reduced models are needed. Machine learning has therefore become a powerful tool, enabling for example fast surrogate models for plasma equilibrium solvers and transport modules. Such models are also developed for building sub-system modules integrated into comprehensive physics-engineering digital twin environments. In some cases, optimization is such that these models can be integrated into real-time control systems. These different applications of machine learning to magnetic fusion challenges will be addressed in this minisymposium. The talks will provide an overview of the advanced ML techniques applied and should thus be of interest to a broad audience.