Pod-galerkin reduced order models and physics-informed neural networks for solving inverse problems for the navier-stokes equations

HIGHLIGHTS

• who: Saddam Hijazi from the Institute of Mathematics, University of Potsdam, Potsdam, Germany have published the article: POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations, in the Journal: (JOURNAL)
• what: The authors focus on ROMs for fluid dynamics problems in the context of the reduction of the Navier-Stokes Equations (NSE). This work shows that training the PINNs by only minimizing the loss function that corresponds to the reduced equations does not give as accurate results as the ones obtained by the projection of . . .
  

   

  

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