9 citations · 9 across the 2 of their papers we have counts for
3 papers
Towards solving General Relativity with Physics-Informed Neural Networks
Juan A. Carretero, Jorge F. Urbán, Fernando Abalos +4
Physics-Informed Neural Networks (PINNs) are a machine-learning framework for approximating solutions to systems of partial differential equations by constraining neural networks t…
MHDuet : a high-order General Relativistic Radiation MHD code for CPU and GPU architectures
Carlos Palenzuela, Miguel Bezares, Steven Liebling +12
We present MHDuet, an open source evolution code for general relativistic magnetohydrodynamics with neutrino transport. The code solves the full set of Einstein equations coupled t…
Global high-order numerical schemes for the time evolution of the general relativistic radiation magneto-hydrodynamics equations
Manuel R. Izquierdo, Lorenzo Pareschi, Borja Miñano +2
Modeling correctly the transport of neutrinos is crucial in some astrophysical scenarios such as core-collapse supernovae and binary neutron star mergers. In this paper, we focus o…