{
  "abstract": "Recent high-sensitivity and high-resolution surveys of the Third Galactic Quadrant [$219.75 \\leq l \\leq 229.75$ and $-5.25 \\leq b \\leq 5.25$], such as the Milky Way Imaging Scroll Painting ( MWISP) survey, have revealed a significant population of molecular clouds (MCs) that appear virially unbound ($α_{vir} > 2$) according to traditional isolated MC models. However, these apparently unstable cloud structures are actually found long-lived and actively undertaking star formation. This inconsistency presents a Virial Paradox. In the present work, we employ a Physics-Informed Neural Network (PINN) to address this discrepancy by re-evaluating the stability of 2,091 MCs . Unlike traditional machine learning, our PINN model embeds the complete Eulerian Virial Theorem including external surface pressure support ($P_{ext}$) directly into the network's loss function. This model autonomously discovers a global Galactic pressure field that scales inversely with vertical height ($|z|$) and Galactocentric radius ($R_{GC}$), consistent with the properties of the Galactic Flare. By incorporating this AI-derived pressure support, we demonstrate a dramatic shift in the stability census: 99.9\\% of previously thought unbound clouds are found to be in a state of pressure confined equilibrium ($α_{AI} \\leq 2$). Furthermore, we find a robust log-log correlation ($R = 0.64$) between our corrected stability parameter and the star formation rate (SFR). This correlation provides independent physical validation that environmental confinement is a primary regulator of star forming efficiency in the outer Galaxy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03450",
  "author_total": 1,
  "authors": [
    {
      "name": "Ashok Mondal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/acec4acf2re6s.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03450",
  "fulltext_url": null,
  "id": "acec4acf2re6s",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03450",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.GA"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03450",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/acec4acf2re6s.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:04Z",
      "native_id": "2611.03450",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03450"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    }
  ],
  "title": "A Physics Informed Neural Network Approach to Decipher Environmental Confinement of Molecular Clouds in the Third Galactic Quadrant",
  "title_text": "A Physics Informed Neural Network Approach to Decipher Environmental Confinement of Molecular Clouds in the Third Galactic Quadrant",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:21Z",
  "url": "https://research.draconitic.com/paper/acec4acf2re6s",
  "venue": "arXiv",
  "versions": []
}