{
  "abstract": "We present a machine-learning-accelerated plasma simulation scheme that combines learned time jumps with ordinary physics evolution. A neural network corrects a low-cost physics forecast over multiple time steps, while the original solver reconstructs the state and advances it between jumps through short reanchoring intervals. The scheme is implemented in PASCHEN-1D, a one-dimensional drift-diffusion-Poisson solver, achieving up to $4.70 \\times$ CPU rollout speedup relative to the ordinary solver for nanosecond pulsed nitrogen discharges. The simulations capture pre-breakdown evolution, rapid sheath formation, expansion and collapse under pulsed-voltage excitation, and post-pulse afterglow. Bulk densities and electric fields agree closely with the ordinary-solver reference, while cathode sheath width, gap voltage, and discharge current reproduce the principal temporal features. The largest spatial discrepancies occur in the cathode sheath during fast transients. A sweep of the physics-reanchoring duration reveals a trade-off between accuracy, acceleration, and numerical stability over the tested 500 ns simulation interval. A model trained on simulations with 1000 grid points also shows useful transfer to 500- and 1500-point meshes without retraining. These results demonstrate a practical route to accelerating plasma simulations by augmenting an established physics solver with machine learning.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23358",
  "author_total": 2,
  "authors": [
    {
      "name": "Asif Iqbal"
    },
    {
      "name": "Peng Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cjynw7qhcajd8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23358",
  "fulltext_url": null,
  "id": "cjynw7qhcajd8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23358",
  "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.comp-ph"
    ],
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "2208|Plasma Diagnostics and Applications"
    ]
  ],
  "openalex": "W7214039375",
  "pdf_url": "https://arxiv.org/pdf/2609.23358",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cjynw7qhcajd8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.23358",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23358"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Machine Learning Accelerated Plasma Simulation through Physics Guided Time Jumps",
  "title_text": "Machine Learning Accelerated Plasma Simulation through Physics Guided Time Jumps",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/cjynw7qhcajd8",
  "venue": "arXiv",
  "versions": []
}