论文标题
符合分数等温气体的人工神经网络建模
Artificial Neural Network Modeling of the Conformable Fractional Isothermal Gas Spheres
论文作者
论文摘要
等温气球是一种特殊类型的车道填充方程，可广泛用于模拟天体物理学中的许多问题，例如恒星，恒星簇和星系的形成。在本文中，我们提出了一种计算方案，旨在使用人工神经网络（ANN）技术模拟符合的分数等温气体球，并将获得的结果与使用Taylor系列推导的分析解决方案进行比较。我们执行了计算，训练了ANN，并使用宽范围的分数参数对其进行了测试。除了emden函数外，我们还计算了分数等温气球的质量 - 拉迪乌斯关系和密度曲线。如果ANN可以完美地模拟一致的分数等温气体，则获得的结果。
The isothermal gas sphere is a particular type of Lane-Emden equation and is used widely to model many problems in astrophysics like stars, star clusters, and the formation of galaxies. In this paper, we present a computational scheme to simulate the conformable fractional isothermal gas sphere using an artificial neural network (ANN) technique and compare the obtained results with the analytical solution deduced using the Taylor series. We performed our calculations, trained the ANN, and tested it using a wide range of the fractional parameter. Besides the Emden functions, we calculated the mass-radius relations and the density profiles of the fractional isothermal gas spheres. The results obtained provided that ANN could perfectly simulate the conformable fractional isothermal gas spheres.
