{
  "abstract": "Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations. We present PreVAE-Turb, a surrogate modeling framework that leverages pre-trained variational autoencoders (VAEs) from the Stable Diffusion image generation model for efficient spatial compression of turbulence fields. The pre-trained VAE is fine-tuned on turbulence data using a physics-informed loss function that includes a spectral loss operating in Fourier space to enforce spectral accuracy across scales. The VAE is combined with convolutional long short-term memory (ConvLSTM) networks to learn temporal dynamics in latent space, with a manifold consistency error metric that monitors encode--decode consistency during autoregressive rollouts. We validate the framework on two-dimensional Hasegawa-Wakatani drift-wave turbulence and extend it to gyrokinetic turbulence from the GENE code, where a four-channel adaptation simultaneously predicts electrostatic potential, density, and parallel/perpendicular temperature fluctuations without requiring architecture redesign. Once trained, inference generates thousands of time steps in seconds on a single GPU, providing substantial computational acceleration compared to direct numerical simulation. The pre-trained approach offers a transferable methodology broadly applicable to various turbulence simulation codes.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38438",
  "author_total": 5,
  "authors": [
    {
      "name": "Minglei Yang"
    },
    {
      "name": "Marshall Nicholson"
    },
    {
      "name": "Diego Del-Castillo-Negrete"
    },
    {
      "name": "David Hatch"
    },
    {
      "name": "Guannan Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6zd9znc4rj74g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38438",
  "fulltext_url": null,
  "id": "6zd9znc4rj74g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38438",
  "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.LG"
    ],
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7215037123",
  "pdf_url": "https://arxiv.org/pdf/2609.38438",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6zd9znc4rj74g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:48Z",
      "native_id": "2609.38438",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38438"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling",
  "title_text": "A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:48Z",
  "url": "https://research.draconitic.com/paper/6zd9znc4rj74g",
  "venue": "arXiv",
  "versions": []
}