Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics
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 sop...
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Auteurs principaux: | Tobias Dieselhorst, William Cook, Sebastiano Bernuzzi, David Radice |
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Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
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Sujets: | |
Accès en ligne: | https://doaj.org/article/4610711e64c94f5eaf8c22d88fb09f53 |
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