{
  "abstract": "The multiscale nature of turbulent reacting flows and the importance of fine-scale structures pose challenges for super-resolution. We investigate a 3D super-resolution framework inspired by Neural Radiance Fields (NeRF), called Neural Field Super-Resolution (NFSR), to reconstruct fine-resolution fields from coarsened reacting-flow fields. The proposed formulation adapts the continuous coordinate-conditioned representation of NeRF to physical flow-field reconstruction by conditioning spatial coordinates with local coarse-grid thermochemical information, while eliminating radiance prediction and volume rendering. Principal component analysis is used to provide a compact and low-dimensional representation of the thermochemical state, and a hierarchical extension, H-NFSR, progressively reconstructs the solution through intermediate resolution levels. The framework is evaluated using three-dimensional direct numerical simulation data for a turbulent premixed hydrogen-air flame, with an overall spatial coarsening factor of eight in each coordinate direction. The results demonstrate consistently better prediction measures and a higher ability to reconstruct fine-scale information than tricubic interpolation on the unseen test realization. The hierarchical formulation reduces visible coarse-cell imprinting. In contrast, biased sampling mainly benefits the most localized structures. Three-dimensional isosurface comparisons further show that H-NFSR recovers much of the small and intermediate features of the flow, including surface wrinkling and local flame curvature effects. These results highlight the potential of coordinate-conditioned neural fields as a flexible framework for three-dimensional coarse-to-fine reconstruction of reacting-flow simulations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.27284",
  "author_total": 3,
  "authors": [
    {
      "name": "Dakshaka Vishwajith Amarathunga"
    },
    {
      "name": "Shang-Ru Li"
    },
    {
      "name": "Tarek Echekki"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4bfbxmcjsjvav.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.27284",
  "fulltext_url": null,
  "id": "4bfbxmcjsjvav",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.27284",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7223123864",
  "pdf_url": "https://arxiv.org/pdf/2610.27284",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4bfbxmcjsjvav.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:04Z",
      "native_id": "2610.27284",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.27284"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "NeRF-Inspired Neural Fields for 3D Super-resolution of Reacting-Flows",
  "title_text": "NeRF-Inspired Neural Fields for 3D Super-resolution of Reacting-Flows",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:04Z",
  "url": "https://research.draconitic.com/paper/4bfbxmcjsjvav",
  "venue": "arXiv",
  "versions": []
}