{
  "abstract": "In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and temperature, $κ_\\perp(n,T)$. The method combines radial profile measurements of electron density and temperature with the residual of a reduced one-dimensional SOL transport equation, so that the inferred conductivity is constrained by both the measurements and the underlying transport model. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles as functions of the radial coordinate and transported power, while a third represents the effective conductivity as a function of the local density and temperature. The framework is first validated using synthetic data generated from a prescribed conductivity function, allowing the inferred $κ_\\perp(n,T)$ to be compared directly with the ground truth. The model recovers the imposed functional dependence with errors below $10~\\%$ in the data-constrained region. Bootstrap resampling is shown to provide a practical indicator of prediction reliability and consistency. A scan in the number of plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability. Finally, the method is applied to an experimental dataset from the TJ-II stellarator obtained with the helium-beam diagnostic. This exploratory application provides an initial estimate of the effective SOL conductivity and illustrates the potential of inverse PINNs for extracting transport information from plasma edge measurements.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.11628",
  "author_total": 9,
  "authors": [
    {
      "name": "J. Gallego"
    },
    {
      "name": "P. Protopapas"
    },
    {
      "name": "A. Bustos"
    },
    {
      "name": "A. Alonso"
    },
    {
      "name": "S. Barquero"
    },
    {
      "name": "A. Baciero"
    },
    {
      "name": "I. Rivera"
    },
    {
      "name": "J. A. Moríñigo"
    },
    {
      "name": "R. Mayo-García"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7sg0rzwwwkdpp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.11628",
  "fulltext_url": null,
  "id": "7sg0rzwwwkdpp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.11628",
  "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",
      "cs.AI"
    ],
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "3106|Magnetic confinement fusion research"
    ]
  ],
  "openalex": "W7212221465",
  "pdf_url": "https://arxiv.org/pdf/2609.11628",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7sg0rzwwwkdpp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2609.11628",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.11628"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices",
  "title_text": "Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:26Z",
  "url": "https://research.draconitic.com/paper/7sg0rzwwwkdpp",
  "venue": "arXiv",
  "versions": []
}