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A mesh-free method for interface problems using the deep learning approach

机译:使用深度学习方法的界面问题的无网线方法

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In this paper, we propose a mesh-free method to solve interface problems using the deep learning approach. Two types of PDEs are considered. The first one is an elliptic PDE with a discontinuous and high-contrast coefficient. While the second one is a linear elasticity equation with discontinuous stress tensor. In both cases, we represent the solutions of the PDEs using the deep neural networks (DNNs) and formulate the PDEs into variational problems, which can be solved via the deep learning approach. To deal with inhomogeneous boundary conditions, we use a shallow neural network to approximate the boundary conditions. Instead of using an adaptive mesh refinement method or specially designed basis functions or numerical schemes to compute the PDE solutions, the proposed method has the advantages that it is easy to implement and is mesh-free. Finally, we present numerical results to demonstrate the accuracy and efficiency of the proposed method for interface problems. (C) 2019 Elsevier Inc. All rights reserved.
机译:在本文中,我们提出了一种使用深度学习方法解决界面问题的网眼方法。考虑两种类型的PDE。第一个是具有不连续和高对比度系数的椭圆PDE。虽然第二个是具有不连续压力张量的线性弹性方程。在这两种情况下,我们代表PDE的解决方案使用深神经网络(DNN)并将PDE配制成变分问题,这可以通过深度学习方法解决。为了处理不均匀的边界条件,我们使用浅神经网络来近似边界条件。代替使用自适应网格细化方法或特殊设计的基本功能或数字方案来计算PDE解决方案,所提出的方法具有易于实现和无网的优点。最后,我们展示了数值结果,以证明所提出的界面问题方法的准确性和效率。 (c)2019 Elsevier Inc.保留所有权利。

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