PINN-DG replaces pointwise derivative losses with finite element interpolation plus discontinuous Galerkin consistency and penalty terms, and proves convergence of the discrete minimizers.
Physics informed neural networks (PINNs) for approximating nonlinear dispersive PDEs
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PINN-DG: Residual neural network methods trained with Finite Elements
PINN-DG replaces pointwise derivative losses with finite element interpolation plus discontinuous Galerkin consistency and penalty terms, and proves convergence of the discrete minimizers.