First neural-network subgrid model for special relativistic MHD reproduces 4x-higher-resolution magnetic field amplification in 3D Kelvin-Helmholtz tests at 44x speedup.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
astro-ph.HE 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
unclear 1representative citing papers
High-resolution GR neutrino-radiation MHD simulation of 1.35-1.35 Msun BNS merger shows KHI-driven B-field amplification to magnetar levels (~10^50 erg, factor >=316) in 3 ms post-merger.
3D GRMHD simulations with second-moment neutrino transport show aligned spins produce more collimated polar outflows and 2.4e-3 solar masses of proton-rich material yielding light r-process elements like 56Ni, while antialigned spins disrupt magnetic amplification.
citing papers explorer
-
Subgrid Modelling for Relativistic Magnetohydrodynamics with Machine Learning
First neural-network subgrid model for special relativistic MHD reproduces 4x-higher-resolution magnetic field amplification in 3D Kelvin-Helmholtz tests at 44x speedup.
-
A magnetar formation in binary neutron star merger
High-resolution GR neutrino-radiation MHD simulation of 1.35-1.35 Msun BNS merger shows KHI-driven B-field amplification to magnetar levels (~10^50 erg, factor >=316) in 3 ms post-merger.
-
Magnetic Eruption and Nucleosynthesis in GR{\nu}MHD Simulations of Spinning Neutron Star Mergers
3D GRMHD simulations with second-moment neutrino transport show aligned spins produce more collimated polar outflows and 2.4e-3 solar masses of proton-rich material yielding light r-process elements like 56Ni, while antialigned spins disrupt magnetic amplification.