arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.03347math.NAcs.LGcs.NAmath-phmath.MP

用于求解偏微分方程的切比雪夫谱神经网络

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations

School of Mathematical Sciences, Shanghai Jiao Tong University(上海交通大学数学科学学院);Institute of Natural Sciences, Shanghai Jiao Tong University(上海交通大学自然科学研究院)

Pengsong Yin, Shuo Ling, Wenjun Ying

AI总结:

本文提出切比雪夫谱神经网络(CSNN),利用满足边界条件的切比雪夫谱神经元和自动微分求解偏微分方程,避免非稀疏线性系统,提升高维问题训练效率与精度,并在椭圆型方程上验证优于PINN。

摘要:

本研究的目的是利用切比雪夫谱方法神经网络(CSNN)模型来求解微分方程。该方法采用单层神经网络,其中使用切比雪夫谱方法构造满足边界条件的神经元。本研究使用前馈神经网络模型和误差反向传播原理,利用自动微分(AD)计算损失函数。该方法避免了求解非稀疏线性系统的需要,便于算法实现和求解高维问题。独特的采样方法和神经元架构显著提高了神经网络的训练效率和精度。此外,多个网络使切比雪夫谱方法能够处理更复杂域上的方程。通过在椭圆型偏微分方程上的测试,研究了CSNN模型的数值效率和精度,并与著名的物理信息神经网络(PINN)方法进行了比较。

英文摘要:

The purpose of this study is to utilize the Chebyshev spectral method neural network(CSNN) model to solve differential equations. This approach employs a single-layer neural network wherein Chebyshev spectral methods are used to construct neurons satisfying boundary conditions. The study uses a feedforward neural network model and error backpropagation principles, utilizing automatic differentiation (AD) to compute the loss function. This method avoids the need to solve non-sparse linear systems, making it convenient for algorithm implementation and solving high-dimensional problems. The unique sampling method and neuron architecture significantly enhance the training efficiency and accuracy of the neural network. Furthermore, multiple networks enables the Chebyshev spectral method to handle equations on more complex domains. The numerical efficiency and accuracy of the CSNN model are investigated through testing on elliptic partial differential equations, and it is compared with the well-known Physics-Informed Neural Network(PINN) method.

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。

↑