{
  "abstract": "Full-wave calculations of ion cyclotron resonance heating (ICRH) under different plasma dielectric conditions require repeated assembly and solution of large-scale discretised systems, limiting parameter sweeps and multi-case response analysis. We therefore propose an anisotropic Maxwell neural operator (AMNO) for rapid parametric modelling of ICRH full-wave responses for the Experimental Advanced Superconducting Tokamak (EAST), which learns, within the one-parameter dielectric-field family generated by varying the hydrogen minority fraction X_H over 0.01-0.05 under otherwise fixed settings, a shared solution operator from the spatially varying complex anisotropic dielectric-tensor field to the three-component complex electric field under frequency-domain Maxwell constraints. It represents global spatial coupling through spectral operator layers and local fine-scale responses, and combines sparse reference-field supervision with the frequency-domain Maxwell-equation residual. Comparisons with COMSOL reference solutions for the same EAST frequency-domain Maxwell-dielectric model show that AMNO reconstructs the principal spatial and spectral features and maintains stable accuracy for unseen interpolation test cases. With reference-field points reduced to 7.5% of the dense full-wave set, AMNO reduces the relative L_2 error by 66.1%-89.9% compared with a sparsely supervised Fourier neural operator (FNO-Sparse) under the same supervision and requires about 0.25 s for single-case inference. AMNO thus reduces dependence on dense reference-field supervision while enabling subsecond parametric complex-field inference, providing a physics-constrained and data-efficient surrogate for rapid in-range X_H sweeps and cross-case response analysis within the modelled EAST configuration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.27490",
  "author_total": 9,
  "authors": [
    {
      "name": "Heng Zhang"
    },
    {
      "name": "Xu Wang"
    },
    {
      "name": "Jiayi Li"
    },
    {
      "name": "Miao Zhang"
    },
    {
      "name": "Jiahui Zhang"
    },
    {
      "name": "Kaihao Wang"
    },
    {
      "name": "Yangdi Yi"
    },
    {
      "name": "Qin Hang"
    },
    {
      "name": "Xinjun Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ae5vdg9kt0dj4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.27490",
  "fulltext_url": null,
  "id": "ae5vdg9kt0dj4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.27490",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7204813498",
  "pdf_url": "https://arxiv.org/pdf/2608.27490",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ae5vdg9kt0dj4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "2608.27490",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.27490"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Anisotropic Maxwell neural operator for rapid parametric full-wave modelling of ion cyclotron resonance heating",
  "title_text": "Anisotropic Maxwell neural operator for rapid parametric full-wave modelling of ion cyclotron resonance heating",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/ae5vdg9kt0dj4",
  "venue": "arXiv",
  "versions": []
}