Artificial Neural Network Modeling of the Conformable Fractional
  Isothermal Gas Spheres
  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.
