{
  "id": 4570283,
  "title": "Conformal Uncertainty Quantification Guarantees for Neural Operators",
  "url": "https://urgent.news/2026/08/28/conformal-uncertainty-quantification-guarantees-for-neural-operators",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-28T16:50:57.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.28515v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Neural operators provide fast surrogate models for approximating operators between function spaces, but their predictions often lack uncertainty quantification. We develop a split conformal framework to guarantee that a calibrated pointwise band around the neural operator output contains the true solution on at least a $1-γ$ fraction of the evaluation domain, with probability at least $1-α$ over…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}