Solver-in-the-Loop Applications in Astrophysical (Magneto)hydrodynamics

Leonard Storcks, Tobias Buck

Keywords: differentiable programming, MHD, fluid dynamics, radiative cooling, stellar wind

Abstract: We present two promising applications of training machine learning models inside a differentiable astrophysical (magneto)hydrodynamics simulator. First, we address the problem of slow convergence in hydrodynamical simulations of wind-blown bubbles with radiative cooling. We demonstrate that a learned cooling function can recover high-resolution dynamics in low-resolution simulations. Secondly, we train a convolutional neural network to correct 2D magnetohydrodynamics simulations of a specific blast wave problem. These case studies pave the way for the principled application of more general machine learning models inside astrophysical simulators. The code is available open source under https://github.com/leo1200/eurips25corr.

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Submission Number: 26