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diffhydro: Inverse Multiphysics Modeling and Embedded Machine Learning in Astrophysical Flows

  • Authors: Benjamin Horowitz, Zarija Lukić, Kentaro Nagamine, Yuri Oku

Benjamin Horowitz et al 2026 The Astrophysical Journal 1012 .

  • Provider: AAS Journals

Caption: Figure 3.

Example output from the cooling/heating test problem in diffhydro. Shown are midplane slices through an 3843 ISM-like box evolved from turbulent initial conditions with Ornstein–Uhlenbeck (OU) forcing and the cooling/heating module enabled. Top: logarithmic gas temperature, spanning the cold to warm neutral phases, with cooling producing sharp temperature contrasts in compressed regions. Bottom: logarithmic mass density in the same slice, exhibiting a connected network of overdense filaments and shells generated by the driven turbulence. The close spatial correspondence between cold structures and density enhancements, and the warm/hot volume-filling background, illustrates the emergence of a multiphase medium and demonstrates that the implemented cooling/heating routines couple self-consistently to the turbulent flow.

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