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Approximation Solution of Fractional Partial Differential Equations by Neural Networks

机译:分数阶偏微分方程的神经网络逼近解

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摘要

Neural networks with radial basis functions method are used to solve a class of initial boundary value of fractional partial differential equations with variable coefficients on a finite domain. It takes the case where a left-handed or right-handed fractional spatial derivative may be present in the partial differential equations. Convergence of this method will be discussed in the paper. A numerical example using neural networks RBF method for a two-sided fractional PDE also will be presented and compared with other methods.
机译:利用径向基函数法的神经网络求解一类具有有限系数的变分式偏微分方程的初始边界值。在这种情况下,偏微分方程中可能存在左手或右手的分数空间导数。本文将讨论这种方法的收敛性。还将给出一个使用神经网络RBF方法求解双面分数PDE的数值示例,并将其与其他方法进行比较。

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