A mesh-free method using piecewise deep neural network for elliptic interface problems

In this paper, we propose a novel mesh-free numerical method for solving the elliptic interface problems based on deep learning. We approximate the solution by the neural networks and, since the solution may change dramatically across the interface, we employ different neural networks for each sub-domain. By reformulating the interface problem as a least-squares problem, we discretize the objective function using mean squared error via sampling and solve the proposed deep least-squares method by standard training algorithms such as stochastic gradient descent. The discretized objective function utilizes only the point-wise information on the sampling points and thus no underlying mesh is required. Doing this circumvents the challenging meshing procedure as well as the numerical integration on the complex interfaces. To improve the computational efficiency for more challenging problems, we further design an adaptive sampling strategy based on the residual of the least-squares function and propose an adaptive algorithm. Finally, we present several numerical experiments in both 2D and 3D to show the flexibility, effectiveness, and accuracy of the proposed deep least-square method for solving interface problems. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

A novel mesh-free numerical method based on deep learning is proposed in this paper for solving elliptic interface problems. By approximating the solution with neural networks and employing different networks for each sub-domain, the method successfully addresses the challenge of dramatic solution changes across the interface. The objective function is discretized using mean squared error via sampling, removing the need for meshing and numerical integration on complex interfaces.