{
  "abstract": "Machine learning has shown promise for physics applications, but integrating Python-based libraries into high-performance C++ solvers can create performance bottlenecks. We present CodeJeNN, which addresses this gap by auto-generating self-contained C++ code from trained Keras models for inference. This eliminates external dependencies through inlined functions, allowing seamless integration into existing frameworks. We describe the Keras-to-C++ workflow, supported architectures, and limitations. CodeJeNN is demonstrated through inference benchmarks against Keras (eager and optimized) and a computational fluid dynamics (CFD) simulation of a hydrogen-air mixing layer in which viscosity is modeled using a neural network, showing that the generated models integrate directly into a solver while remaining efficient and accurate.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.02746",
  "author_total": 5,
  "authors": [
    {
      "name": "Jay Arcities"
    },
    {
      "name": "Pavel Popov"
    },
    {
      "name": "Eric J Ching"
    },
    {
      "name": "Kamal Viswanath"
    },
    {
      "name": "Ryan F Johnson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fnng4dsz3eqf5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.02746",
  "fulltext_url": null,
  "id": "fnng4dsz3eqf5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.02746",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7167610672",
  "pdf_url": "https://arxiv.org/pdf/2607.02746",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fnng4dsz3eqf5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:15Z",
      "native_id": "2607.02746",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.02746"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "CodeJeNN: A simple C++ neural network generator for physics applications",
  "title_text": "CodeJeNN: A simple C++ neural network generator for physics applications",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:15Z",
  "url": "https://research.draconitic.com/paper/fnng4dsz3eqf5",
  "venue": "arXiv",
  "versions": []
}