Asymptotic preserving scheme for anisotropic elliptic equations with deep neural network

In this paper, a new asymptotic preserving (AP) scheme is proposed for the anisotropic elliptic equations. Different from previous AP schemes, the actual one is based on first-order system least-squares for second-order partial differential equations, and it is uniformly well-posed with respect to anisotropic strength. The numerical computation is realized by a deep neural network (DNN), where least-squares functionals are employed as loss functions to determine parameters of DNN. Numerical results show that the current AP scheme is easy for implementation and is robust to approximate solutions or to identify anisotropic strength in various 2D and 3D tests

Preserving the accuracy of numerical methods discretizing anisotropic elliptic problems

In this paper we study the loss of precision of numerical methods discretizing anisotropic problems and propose alternative approaches free from this drawback. The deterioration of the accuracy is observed when the coordinates and the mesh are unrelated to the anisotropy direction. While this issue is commonly addressed by increasing the scheme approximation order, we demonstrate that, though the gains are evident, the precision of these numerical methods remain far from optimal and limited to moderate anisotropy strengths. This is analysed and explained by an amplification of the approximation error related to the anisotropy strength. We propose an approach consisting in the introduction of an auxiliary variable aimed at removing the amplification of the discretization error. By this means the precision of the numerical approximation is demonstrated to be independent of the anisotropy strength