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Neural networks enable real-time plasma control in fusion reactors

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Neural networks enable real-time plasma control in fusion reactors

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alasdair Ross, George K. Holt, Kamran Pentland, Adriano Agnello, Nicola C. Amorisco, Pedro Cavestany, Aran Garrod, Timothy Nunn, Charles Vincent, Graham McArdle ·

    Real-time virtual circuits for plasma shape control via neural network emulators

    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{virtu…

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