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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 17.

A visualization of the Kelvin–Helmholtz flux corrector network on a test initial conditions set. Despite only training our model up until t = 1.0, we are able to maintain numerical stability well past t = 3.0. This is seen both in terms of the recovered density structures as well as the position/number of the vortexes seen in the energy field.

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