Artificial Neural Network Approach for Relativistic Polytropes

Abstract

Over the last few decades, artificial neural networks (ANN) have played an essential role in many areas of human activity and have found application in many branches of natural sciences. ANNs have been widely used to tackle problems related to linear and nonlinear differential equations, and numerous paradigms for ANN architecture have been employed. This research proposes a computational technique based on ANN and the Taylor series to solve difficulties connected to the relativistic gas spheres' Tolman-Oppenheimer-Volkoff equations (TOV). We used ANN to study two cases related to relativistic polytropes. The first is to simulate both the Emden and the relativistic functions. The second is to predict the zeros of the Emden function and its corresponding relativistic functions. In its feed-forward back-propagation learning scheme, we used the ANN framework. The efficiency of the proposed algorithm is evaluated by running it through seven models. Comparing the analytical and the ANN solutions gives good agreement for the two cases under study.