{
  "id": 18244,
  "url": "https://arxiv.org/abs/2608.09921v1",
  "title": "GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis",
  "summary": "Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced. We present GENCO (GEometric Neural Corrective Optimizer), a unified neural solver for steady-state transmission grid analysis that handles power flow (PF), optimal power flow (OPF), and state estimation (SE) within a single architecture and shared network representation. To suppo",
  "authors": "Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy, Mangaliso Mngomezulu, Héctor Maeso-García, Thomas Tolhurst, Javad Bayazi, Ali Moeini, Naomi Simumba, Celia Cintas, David Nelischer, Romeo Kienzler, Jonas Weiss, Anna Varbella, Florian Dörfler, Gabriela Hug, Martin Mevissen, Juan Bernabé-Moreno, François Mirallès, Hendrik F. Hamann, Etienne Vos, Thomas Brunschwiler",
  "category": "research",
  "topics": "jobs-economy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T17:57:49.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18244",
  "original_url": "https://arxiv.org/abs/2608.09921v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}