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    "story_id": "mp-2026-09-14-004",
    "source_story_id": "tmp-story-du-no-wave-operator",
    "edition_id": "mp-2026-09-14-morning-0067",
    "edition_url": "https://themachinepress.com/edition/2026-09-14",
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    "headline": "A Wave Model Kept the Detail With One-Tenth the Parameters",
    "slug": "a-wave-model-kept-the-detail-with-one-tenth-the-parameters",
    "dek": "DU-NO used 3.64 million parameters, improved rollout error 14.9% over U-FNO and preserved high-frequency wave structure.",
    "summary": "DU-NO used 3.64 million parameters, improved rollout error 14.9% over U-FNO and preserved high-frequency wave structure.",
    "body_text": "Phase-resolving coastal models are accurate but too expensive for many ensembles and real-time forecasts. DU-NO places lightweight convolutional U-Net branches only on the fine encoder and decoder levels where high-wavenumber content exists, leaving coarser levels spectral. The resulting 3.64-million-parameter neural operator used 10.8 times fewer parameters than U-FNO while improving autoregressive rollout error by 14.9% on the authors’ FUNWAVE-TVD benchmark. A parameter-matched baseline still trailed by 28.6%, and tests extended to Navier–Stokes and shallow-water rollouts. Operational warning performance remains to be established.",
    "why_it_matters": "DU-NO used 3.64 million parameters, improved rollout error 14.9% over U-FNO and preserved high-frequency wave structure.",
    "limitations": [
      "DU-NO places lightweight convolutional U-Net branches only on the fine encoder and decoder levels where high-wavenumber content exists, leaving coarser levels spectral."
    ],
    "importance": 8,
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    "first_published_at": "2026-09-14T09:00:00.000-04:00",
    "modified_at": "2026-09-14T09:00:00.000-04:00",
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        "text": "DU-NO used 3.64 million parameters, improved rollout error 14.9% over U-FNO and preserved high-frequency wave structure.",
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        "qualification": "DU-NO places lightweight convolutional U-Net branches only on the fine encoder and decoder levels where high-wavenumber content exists, leaving coarser levels spectral."
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    "source_ids": [
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    "tags": [
      "neural operators",
      "wave forecasting",
      "efficient models"
    ],
    "image_url": null,
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-09-14-004",
      "title": "arXiv preprint 2609.12115",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2609.12115",
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      "source_type": "primary_research",
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      "published_at": null,
      "accessed_at": "2026-09-14T08:24:00.000-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "A Wave Model Kept the Detail With One-Tenth the Parameters",
    "publisher": "The Machine Press",
    "published_at": "2026-09-14T09:00:00.000-04:00",
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