Learning Neural Operator Surrogates for the Black Hole Accretion Code
General-relativistic magnetohydrodynamic (GR-MHD) simulations are essential for studying black hole accretion, relativistic jets, and magnetic reconnection, yet their computational cost severely limits systematic parameter exploration. We investigate neural operator surrogates for two astrophysically relevant simulation scenarios produced by the Black Hole Accretion Code (\texttt{BHAC}). First, a Physics Informed Fourier Neural Operator (PINO) is trained on the special-relativistic resistive MHD (SRRMHD) evolution of the Orszag-Tang vortex over a range of resistivities spanning the Sweet-Parker and fast reconnection regimes. By embedding the governing equations as an additional loss term ...
| Authors | Matthias Nägele, Cedric Bös, Chester Tan et al. |
|---|---|
| Primary category | astro-ph.HE |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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