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Applications of deep learning to relativistic hydrodynamics

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 نشر من قبل Huichao Song
 تاريخ النشر 2018
  مجال البحث فيزياء
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In this proceeding, we will briefly review our recent progress on implementing deep learning to relativistic hydrodynamics. We will demonstrate that a successfully designed and trained deep neural network, called {tt stacked U-net}, can capture the main features of the non-linear evolution of hydrodynamics, which could also rapidly predict the final profiles for various testing initial conditions.



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