{
  "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 scenario trajectory, with each VC used to control the plasma within a preset time interval. While effective near the reference equilibrium, this approach can lead to degraded performance as the plasma departs from the reference equilibrium and/or from the desired trajectory, and it complicates the design of robust control strategies for rapidly evolving plasma configurations. In this paper, we construct neural-network-based emulators of plasma shape parameters from which VCs can be derived, to provide the MAST Upgrade (MAST-U) plasma control system with state-aware VCs in real-time. To do this, we develop an extensive library of over a million simulated GS equilibria, covering a substantial portion of the MAST-U operational space. These emulators provide differentiable functions whose gradients can be rapidly computed, enabling the derivation of accurate VCs for real-time shape control. We perform extensive verification of the emulated VCs by testing whether they disentangle the control problem. The neural-network-based approach delivers high accuracy and orthogonality across a diverse range of equilibria. This work establishes the physical validity of emulated VCs as a scalable and general alternative to schedules of precomputed VCs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.14939",
  "author_total": 10,
  "authors": [
    {
      "name": "Alasdair Ross"
    },
    {
      "name": "George K. Holt"
    },
    {
      "name": "Kamran Pentland"
    },
    {
      "name": "Adriano Agnello"
    },
    {
      "name": "Nicola C. Amorisco"
    },
    {
      "name": "Pedro Cavestany"
    },
    {
      "name": "Aran Garrod"
    },
    {
      "name": "Timothy Nunn"
    },
    {
      "name": "Charles Vincent"
    },
    {
      "name": "Graham McArdle"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f0k7e637mnfh4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.14939",
  "fulltext_url": null,
  "id": "f0k7e637mnfh4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.14939",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "3106|Magnetic confinement fusion research"
    ]
  ],
  "openalex": "W7161350689",
  "pdf_url": "https://arxiv.org/pdf/2605.14939",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f0k7e637mnfh4.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:04Z",
      "native_id": "2605.14939",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.14939"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Real-time virtual circuits for plasma shape control via neural network emulators",
  "title_text": "Real-time virtual circuits for plasma shape control via neural network emulators",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:21Z",
  "url": "https://research.draconitic.com/paper/f0k7e637mnfh4",
  "venue": "arXiv",
  "versions": []
}