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.
翻译:在本文中,为厌食性椭圆方程式提出了一个新的无症状保存(AP)计划。与以往的AP方案不同,实际方案以二阶部分偏差方程式的一阶系统最低方程式为基础,对厌食性强度统一适用。数字计算由一个深层神经网络(DNN)实现,其中最小方程式功能被用作确定DNN参数的损失函数。数字结果显示,目前的AP方案计划易于实施,并且对于近似解决方案或确定2D和3D不同测试中的厌食性强度非常有力。