Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics

@article{Dieselhorst2021MachineLF,
  title={Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics},
  author={Tobias Dieselhorst and William Cook and Sebastiano Bernuzzi and Davide Radice},
  journal={Symmetry},
  year={2021},
  volume={13},
  pages={2157}
}
The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models, such as those required to calculate accurate gravitational wave signals in numerical relativity simulations of binary neutron stars. This work explores the use of… 

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