PINN-DG replaces pointwise derivative losses with finite element interpolation plus discontinuous Galerkin consistency and penalty terms, and proves convergence of the discrete minimizers.
A unified deep artificial neural network approach to partial differential equations in complex geometries
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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.