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Advanced Modeling and Simulation in Engineering Sciences
Fig. 16 | Advanced Modeling and Simulation in Engineering Sciences

Fig. 16

From: POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations

Fig. 16

The drag coefficients curve over the time range \([100,150]\,\textrm{s}\). The figures show the one obtained by the FOM solver and the ones approximated by the ROM for different values of and \(N_u\), \(N_p\) and \(N_S\). (a) The drag coefficients curve reconstructed by the ROM using \(N_u=N_p=N_S=10\) compared to the FOM one. (b) The drag coefficients curve reconstructed by the ROM using \(N_u=N_p=N_S=20\) compared to the FOM one. (c) The drag coefficients curve reconstructed by the ROM using \(N_u=N_p=N_S=30\) compared to the FOM one. (d) The drag coefficients curve reconstructed by the ROM using \(N_u=N_p=N_S=60\) compared to the FOM one

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