arXiv:2605.14939v2 Announce Type: replace-cross Abstract: Reliable position and shape control in tokamak plasmas requires accurate real-time regulation of several strongly coupled shape parameters. The control vectors that disentangle these couplings, referred to as \textit{virtual circuits} (VCs), enable independent shape parameter control for a specific Grad--Shafranov (GS) equilibrium. Numerical calculation of VCs is not currently feasible in real time, therefore VCs are usually computed prior to each experiment, using a small number of reference GS equilibria sampled along the desired scen
The increasing sophistication of AI, particularly neural networks, is making real-time control applications feasible now, overcoming previous computational bottlenecks in complex physics systems.
This development significantly advances the potential for stable and efficient fusion energy, a critical long-term solution for global energy needs, by enabling precise real-time plasma control.
The ability to calculate virtual circuits in real-time using AI removes a major barrier to the operational stability and efficiency of tokamak reactors, accelerating their path to viability.
- · Fusion energy researchers and institutions
- · AI/ML developers in scientific computing
- · Energy utilities and grid operators
- · High-performance computing sector
- · Fossil fuel industries (long-term)
- · Less advanced computational physics methods
More stable and efficient operation of experimental fusion reactors.
Accelerated development and potential commercialization of fusion power plants, leading to a new era of clean, abundant energy.
Reduced reliance on fossil fuels globally, fundamentally altering energy geopolitics and mitigating climate change.
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