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arXiv 2201.12995math.NAcs.NA
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求解偏微分方程的深度Petrov-Galerkin方法

Deep Petrov-Galerkin Method for Solving Partial Differential Equations

Yong Shang, Fei Wang, Jingbo Sun

摘要:

深度神经网络是逼近函数的强大工具,它们被应用于成功解决许多领域的各种问题。本文提出了一种基于神经网络的求解偏微分方程的数值方法。在该框架下,该方法基于弱形式,未知函数采用深度神经网络逼近,测试函数可以采用不同的方法选择,如有限元法的基函数、神经网络等,由于测试函数和检验函数的空间不同,我们将其命名为深度Petrov-Galerkin方法(DPGM).所得到的线性方程组不一定是对称的和平方的,因此离散化问题用最小二乘法求解。以泊松问题为例,提出并研究了基于多种混合形式的混合DPGM。此外,我们还将DPGM应用于求解两个经典的基于时空方法的时变问题,即用神经网络逼近未知函数,将时间变量和空间变量同等对待,并将初始条件作为时空域的边界条件。最后,通过数值算例说明了DPGM的性能,并观察到这种新方法在几个方面优于传统的数值方法。

英文摘要:

Deep neural networks are powerful tools for approximating functions, and they are applied to successfully solve various problems in many fields. In this paper, we propose a neural network-based numerical method to solve partial differential equations. In this new framework, the method is designed on weak formulations, and the unknown functions are approximated by deep neural networks and test functions can be chosen by different approaches, for instance, basis functions of finite element methods, neural networks, and so on. Because the spaces of trial function and test function are different, we name this new approach by Deep Petrov-Galerkin Method (DPGM). The resulted linear system is not necessarily to be symmetric and square, so the discretized problem is solved by a least-square method. Take the Poisson problem as an example, mixed DPGMs based on several mixed formulations are proposed and studied as well. In addition, we apply the DPGM to solve two classical time-dependent problems based on the space-time approach, that is, the unknown function is approximated by a neural network, in which temporal variable and spatial variables are treated equally, and the initial conditions are regarded as boundary conditions for the space-time domain. Finally, several numerical examples are presented to show the performance of the DPGMs, and we observe that this new method outperforms traditional numerical methods in several aspects.

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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