{
  "id": 7199,
  "url": "https://arxiv.org/abs/2603.14734v1",
  "title": "Gauge-Equivariant Intrinsic Neural Operators for Geometry-Consistent Learning of Elliptic PDE Maps",
  "summary": "Learning solution operators of partial differential equations (PDEs) from data has emerged as a promising route to fast surrogate models in multi-query scientific workflows. However, for geometric PDEs whose inputs and outputs transform under changes of local frame (gauge), many existing operator-learning architectures remain representation-dependent, brittle under metric perturbations, and sensitive to discretization changes. We propose Gauge-Equivariant Intrinsic Neural Operators (GINO), a cla",
  "authors": "Pengcheng Cheng",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-16T02:10:06.000Z",
  "fetched_at": "2026-07-14T16:33:03.572Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7199",
  "original_url": "https://arxiv.org/abs/2603.14734v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}