{
  "id": 16641,
  "url": "https://arxiv.org/abs/2608.04030",
  "title": "NuclearDiffusion: Text-to-Image Foundation Models for Learning Nuclear Energy Concepts",
  "summary": "arXiv:2608.04030v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) has transformed text-to-image synthesis, yet its ability to represent specialized engineering domains remains largely unexplored. As an exmaple in nuclear engineering, general-purpose foundation models frequently generate physically incorrect or conceptually inconsistent images because they lack domain-specific knowledge. This work presents one of the first systematic studies of domain adaptation for nuclea",
  "authors": "Mohammed I. Radaideh, Jeremy Moon, Andre Gala-Garza, Emma Son, Yug Shah, Majdi I. Radaideh",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T04:00:00.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16641",
  "original_url": "https://arxiv.org/abs/2608.04030",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}