{
  "id": 19301762,
  "title": "Physics-informed neural networks in the small-data regime: A controlled comparison with classical PDE surrogates",
  "url": "https://urgent.news/2026/11/09/physics-informed-neural-networks-in-the-small-data-regime-a",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-09T13:49:33.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.12424v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Neural surrogates for engineering models are often trained on large collections of simulated solutions. We investigate whether physics-informed neural networks (PINNs) can achieve accurate predictions from scarce labelled observations by incorporating governing equations and prescribed initial and boundary conditions. We introduce hierarchical loss weighting (H-PINN) and its…",
  "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."
}