Reconstructing Relativistic Magnetohydrodynamics with Physics-Informed Neural Networks

Corwin Cheung, Marcos Johnson-Noya, Michael Xiang +2

We construct the first physics-informed neural-network (PINN) surrogates for relativistic magnetohydrodynamics (RMHD) using a hybrid PDE and data-driven workflow. Instead of traini…