Two neural-network-based methods for solving obstacle problems

Abstract

Two neural-network-based numerical schemes are proposed to solve the classical obstacle problems. The schemes are based on the universal approximation property of neural networks, and the cost functions are taken as the energy minimization of the obstacle problems. We rigorously prove the convergence of the two schemes and derive the convergence rates with the number of neurons $N$. In the simulations, we use two example problems (1-D & 2-D) to verify the convergence rate of the methods and the quality of the results.

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• Artificial Neural Networks for Solving Ordinary and Partial Differential Equations
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Cited by in corpus (1)

• Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations