Variational Physics-Informed Neural Networks For Solving Partial Differential Equations

Physics-informed neural networks (PINNs) [31] use automatic differentiation to solve partial differential equations (PDEs) by penalizing the PDE in the loss function at a random set of points in the domain of interest. Here, we develop a Petrov-Galerkin version of PINNs based on the nonlinear approximation of deep neural networks (DNNs) by selecting the em trial space to be the space of neural networks and the em test space to be the space of Legendre polynomials. We formulate the textitvariational residual of the PDE using the DNN approximation by incorporating the variational form of the problem into the loss function of the network and construct a textitvariational physics-informed neural network (VPINN). By integrating by parts the integrand in the variational form, we lower the order of the differential operators represented by the neural networks, hence effectively reducing the training cost in VPINNs while increasing their accuracy compared to PINNs that essentially employ delta test functions. For shallow networks with one hidden layer, we analytically obtain explicit forms of the textitvariational residual. We demonstrate the performance of the new formulation for several examples that show clear advantages of VPINNs over PINNs in terms of both accuracy and speed.