DGNet: A Neural Network Framework Induced by Discontinuous Galerkin Methods

We propose a general framework for the Discontinuous Galerkin-induced Neural Network (DGNet) inspired by the Interior Penalty Discontinuous Galerkin Method (IPDGM). In this approach, the trial space consists of piecewise neural network space defined over the computational domain, while the test function space is composed of piecewise polynomials. We demonstrate the advantages of DGNet in terms of accuracy and training efficiency across several numerical examples, including stationary and time-dependent problems. Specifically, DGNet easily handles high perturbations, discontinuous solutions, and complex geometric domains.