Achieving angular-momentum conservation with physics-informed neural networks in computational relativistic spin hydrodynamics

Hidefumi Matsuda, Koichi Hattori, Koichi Murase

We propose physics-informed neural networks (PINNs) as a numerical solver for relativistic spin hydrodynamics and demonstrate that the total angular momentum, i.e., the sum of orbi…