PapersSelected73°8月31日 09:37

Neural network emulators for plasma control

Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade

arXiv cs.AI@Nicola C. Amorisco et al. (15)

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Why we picked it

This method simplifies plasma control workflows, potentially reducing manual preparation time for fusion research facilities in the region.

Researchers deployed real-time virtual circuits using neural network emulators on MAST Upgrade tokamak.

Body

The system replaced pre-set lookup tables with dynamically updated circuits during plasma discharges.Experiments covered shape perturbations, divertor-leg motion, and evolving plasma configurations.

The approach maintains existing control architecture while enabling automatic online generation of control schedules.

Original · arXiv cs.AI

Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade

Conventional plasma shape control in tokamaks relies on virtual circuits (VCs) that are computed offline from linearisations around a small, tailored number of reference equilibria, and deployed as expertly prepared schedules during the discharge. Here, we report on the first experimental deployment of real-time VCs. We replace pre-set look up tables with VCs updated in real time using surrogates of the plasma response. Both the existing control architecture and the interpretability of VC-based control are retained. Previous work showed that neural network emulators can produce accurate VCs, and validated their performance in closed-loop shape control simulations. Here, we report their first experimental validation on MAST Upgrade (MAST-U). Dedicated experiments spanning different scenarios, including prescribed shape perturbations, feedback-driven divertor-leg motion, and strongly evolving plasma configurations, show that real-time VCs can realise plasma shape control tasks within the MAST-U plasma control system. These results establish the experimental feasibility of real-time linearisations as a practical extension of conventional plasma shape control in tokamaks. The present implementation demonstrates a central step towards a simpler control workflow, in which manually constructed, phased VC schedules are replaced by VCs generated automatically online from a trained surrogate model, without scenario-specific retraining.

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include MAST Upgrade, neural network emulators, plasma shape control, tokamaks.

Source attribution: curated from arXiv cs.AI (@Nicola C. Amorisco, Kamran Pentland, Adriano Agnello, George K. Holt, Alasdair Ross, Matthew J. Marshall, Edward Jones, Graham J. McArdle, Charles Vincent, Timothy Nunn, Martin Kochan, Pedro Cavestany, Aran Garrod, Stanislas Pamela, James Buchanan).

#MAST Upgrade #neural network emulators #plasma shape control #tokamaks #fusion research

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