Neural networks enable real-time plasma control in fusion reactors

Researchers have developed neural network emulators to create real-time virtual circuits for controlling plasma shape in tokamak fusion reactors. This approach uses a vast dataset of simulated plasma equilibria to train emulators that can rapidly derive accurate virtual circuits, overcoming the limitations of pre-computed circuits which degrade in performance as plasma conditions deviate from reference points. The validated emulators demonstrate high accuracy and orthogonality, offering a scalable and generalizable alternative for real-time plasma control. AI

IMPACT This research could lead to more stable and efficient plasma control in fusion reactors, accelerating progress towards viable fusion energy.

RANK_REASON The cluster contains an academic paper detailing a new methodology for plasma control using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

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• Grad--Shafranov
• neural network emulators

• paper
• infra

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