New Numerical Insights into Electromagnetic Mass in Spherically Symmetric Configurations

Our study aims to evolve artificial intelligence that emerges from natural processes and be able to predict nonlinearities in the lane Emden-Fowler (LEF) equation. More precisely, the feedforward artificial neural network model is an adaptive one that leads to accurate solutions of the LEF equation. This dataset concerns a neural network whose parameters have been made adjustable so that initial predictions can be based on a given model quite easily. The energy reduction objective function for the specific limitations of the LEF equations is based upon contextual nuances introduced in previous optimization process. To begin with, this proposed methodology has been tested through experiments and initial conditions affecting the initial value of any such problem for LEF. We consider three cases in detail which show how our method can solve the LEF equation effectively. Our combined method (PSO-GWO-IPA) with Particle Swarm Optimizer (PSO) and Grey Wolf Optimizer (GWO) achieves very good convergence speed when compared to PS, IPA, PSO, PS-IPA, HPM and OHM. Through statistical tests, we will verify reliability and validity of our approach mentioned above. Our empirical results are in perfect agreement with the mathematical model, demonstrating the wisdom of the proposed method.