利用人工神经网络技术数值求解埃姆登-福勒方程耦合系统

Ashish Kumar, Manoj Kumar, Parany Goswami
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引用次数: 0

摘要

本文提出了一种深度人工神经网络技术,用于求解埃姆登-福勒方程耦合系统。开发了一种矢量化的算法形式。使用 Python 代码对该技术进行了实现和模拟。我们在各种数值示例中实现了这一技术,并进行了仿真。我们用图表展示了这种方法的精确性。我们使用误差表对数值解和精确解进行了比较。我们还对我们的解法与其他方法进行了比较分析,包括伯恩斯坦配位法和同调分析法。比较结果以误差表的形式呈现。这些图和表证明了这种方法的效率和准确性。
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Numerical solution of coupled system of Emden-Fowler equations using artificial neural network technique
In this paper, a deep artificial neural network technique is proposed to solve the coupled system of Emden-Fowler equations. A vectorized form of algorithm is developed. Implementation and simulation of this technique is performed using Python code. This technique is implemented in various numerical examples, and simulations are conducted. We have shown graphically how accurately this method works. We have shown the comparison of numerical solution and exact solution using error tables. We have also conducted a comparative analysis of our solution with alternative methods, including the Bernstein collocation method and the Homotopy analysis method. The comparative results are presented in error tables. The efficiency and accuracy of this method are demonstrated by these graphs and tables.
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