2025/12/01 by Storcks, Leonard, Buck, Tobias
Engineering · Mathematics · Physics and Astronomy · #Artificial neural network #Code (set theory) #Computational Fluid Dynamics and Aerodynamics #Convergence (economics) #Convolutional neural network #Function (biology) #Magnetohydrodynamics #Model Reduction and Neural Networks #Radiative transfer #Tensor decomposition and applications
paper · open access · doi:10.48550/arxiv.2512.05999
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/12/01 · openalex created_date 2025/12/10 · openalex updated_date 2026/07/28
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.