A Physics Informed Neural Network Approach to Decipher Environmental Confinement of Molecular Clouds in the Third Galactic Quadrant

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.