论文标题
使用人工神经网络对分数多潮流气体进行建模
Modeling Fractional Polytropic Gas Spheres Using Artificial Neural Network
论文作者
论文摘要
车道束微分方程描述了不同的物理和天体物理现象，包括恒星结构，等温气体球，气体球形云热历史和热电流。本文提出了一种计算方法，以解决基于神经网络的分数泳道微分方程有关的问题。这样的解决方案将有助于解决分数多变态气体球体问题，这些问题在物理，天体物理学，工程和几个现实生活中都有不同的应用。我们在其前馈传播学习方案中使用了人工神经网络（ANN）框架。通过在四个分数车道填充方程进行测试，将其效率和准确性与多型指数n = 0,1,5的精确溶液进行比较，并将其序列膨胀的精确溶液进行比较来检查算法的效率和准确性。我们获得的结果证明，使用ANN方法是可行的，准确的，并且可能超过其他方法。
Lane-Emden differential equations describe different physical and astrophysical phenomena that include forms of stellar structure, isothermal gas spheres, gas spherical cloud thermal history, and thermionic currents. This paper presents a computational approach to solve the problems related to fractional Lane-Emden differential equations based on neural networks. Such a solution will help solve the fractional polytropic gas spheres problems which have different applications in physics, astrophysics, engineering, and several real-life issues. We used Artificial Neural Network (ANN) framework in its feedforward back propagation learning scheme. The efficiency and accuracy of the presented algorithm are checked by testing it on four fractional Lane-Emden equations and compared with the exact solutions for the polytopic indices n=0,1,5 and those of the series expansions for the polytropic index n=3. The results we obtained prove that using the ANN method is feasible, accurate, and may outperform other methods.
