{
  "abstract": "High-fidelity three-dimensional turbulent flow fields are essential for scientific and engineering analyses, yet obtaining them through direct numerical simulation or high-resolution measurements remains expensive. Super-resolution reconstruction from coarse-grained data therefore offers a promising alternative, but remains fundamentally challenging because fine-scale structures are intrinsically three-dimensional and dynamically coupled across scales. Existing data-driven approaches still struggle to recover multiscale volumetric structures and their coherent temporal evolution. To address these limitations, we propose a turbulence-aware framework for time-resolved three-dimensional flow reconstruction. The framework couples a multiscale neural operator, designed to recover volumetric structures, with a state space model to promote temporal consistency in the reconstructed dynamics. In addition, Navier-Stokes residuals are incorporated as soft constraints to regularize the reconstruction toward physically plausible dynamics. A mixture-of-experts mechanism is also introduced to support unsupervised adaptation to unknown coarse-graining. Across evaluations on canonical turbulent systems and atmospheric reanalysis data, the proposed framework reduces reconstruction errors relative to learning-based and classical baselines, while better preserving multiscale spatial structure and coherent temporal evolution. These results validate the effectiveness of the proposed framework in reconstructing high-fidelity three-dimensional flow fields from coarse-grained data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.18045",
  "author_total": 5,
  "authors": [
    {
      "name": "Hanlin Kong"
    },
    {
      "name": "Jianan Wei"
    },
    {
      "name": "Yuntian Chen"
    },
    {
      "name": "Yujia Wang"
    },
    {
      "name": "Wenguan Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1a1z9yatr889a.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.18045",
  "fulltext_url": null,
  "id": "1a1z9yatr889a",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.18045",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7221817292",
  "pdf_url": "https://arxiv.org/pdf/2610.18045",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1a1z9yatr889a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:11Z",
      "native_id": "2610.18045",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.18045"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics",
  "title_text": "High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:11Z",
  "url": "https://research.draconitic.com/paper/1a1z9yatr889a",
  "venue": "arXiv",
  "versions": []
}