arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2302.02405math.NAcs.LGcs.NA
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弱解的Deep Galerkin方法的收敛性分析

Convergence Analysis of the Deep Galerkin Method for Weak Solutions

Wuhan University(武汉大学);The Hong Kong University of Science and Technology(香港科技大学);University of Maryland, College Park(马里兰大学帕克分校)

Yuling Jiao, Yanming Lai, Yang Wang, Haizhao Yang, Yunfei Yang

摘要:

在$\mathbb{R}^d$上分析了二阶椭圆型偏微分方程弱解的深度Galerkin方法在Dirichlet、Neumann和Robin边界条件下的收敛速度.在DGMW中,应用深度神经网络来参数化PDE解,并采用第二个神经网络来参数化传统Galerkin公式中的检验函数。通过根据训练样本数$n$适当选择两个网络的深度和宽度,证明了DGMW的收敛速度为$\mathcal{O}(n^{-1/d})$,这是对弱解的第一个收敛结果.证明的主要思想是将DGMW的误差分为近似误差和统计误差。我们推导出一个上界的近似误差的$H^{1}$规范和限制的统计误差通过Rademacher复杂性。

英文摘要:

This paper analyzes the convergence rate of a deep Galerkin method for the weak solution (DGMW) of second-order elliptic partial differential equations on $\mathbb{R}^d$ with Dirichlet, Neumann, and Robin boundary conditions, respectively. In DGMW, a deep neural network is applied to parametrize the PDE solution, and a second neural network is adopted to parametrize the test function in the traditional Galerkin formulation. By properly choosing the depth and width of these two networks in terms of the number of training samples $n$, it is shown that the convergence rate of DGMW is $\mathcal{O}(n^{-1/d})$, which is the first convergence result for weak solutions. The main idea of the proof is to divide the error of the DGMW into an approximation error and a statistical error. We derive an upper bound on the approximation error in the $H^{1}$ norm and bound the statistical error via Rademacher complexity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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