{
  "abstract": "We introduce a physics-constrained neural network for the rapid prediction of rigorous coupled-wave analysis outputs in the form of Jones matrices. Starting from energy conservation in lossless layered periodic structures, we use the fact that the scattering outputs lie on a Stiefel manifold. This energy constraint is enforced as a hard condition by projecting onto the manifold using differentiable symmetric orthogonalization. The resulting surrogate enforces energy conservation by construction while preserving differentiability for gradient-based inverse design. The performance and generality of the proposed approach are demonstrated through the inverse design of a diffractive waveguide combiner for augmented reality glasses.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.28119",
  "author_total": 2,
  "authors": [
    {
      "name": "Eric Prehn"
    },
    {
      "name": "Peter Jung"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6wfxfqbmswr7f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.28119",
  "fulltext_url": null,
  "id": "6wfxfqbmswr7f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.28119",
  "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.optics"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7166529738",
  "pdf_url": "https://arxiv.org/pdf/2606.28119",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6wfxfqbmswr7f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2606.28119",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.28119"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Physics-constrained neural networks for surrogate modeling of lossless periodic structures",
  "title_text": "Physics-constrained neural networks for surrogate modeling of lossless periodic structures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:26Z",
  "url": "https://research.draconitic.com/paper/6wfxfqbmswr7f",
  "venue": "arXiv",
  "versions": []
}