A machine learning neural network architecture for the accelerating universe based modified gravity - Zulqurnain Sabir; Basma Souayeh; Zahraa Zaiour; Alyn Nazal; Mir Waqas Alam; Huda Alfannakh (Egyptian Informatics Journal)
The current investigations present the numerical outputs of the mathematical accelerating universe based modified gravity model (MAUMGM) by designing a computational stochastic structure using the Bayesian regularization neural network. The classification of the mathematical MAUMGM is presented into five different nonlinear classes. A dataset is designed using the explicit Runge-Kutta scheme, which is divided into training as 82% and 9%, 9% for testing and verification. The designed stochastic process for solving the MAUMGM contains log-sigmoid activation function, thirty neurons in the hidden layer, dataset based explicit Runge-Kutta, and Bayesian regularization for the optimization. The correctness of the stochastic solver is perceived by comparing the outcomes along with absolute error 10-06 to 10-09. The best training values are reported around 10-13 to 10-14, which also signify the solver’s perfection. To authenticate the accuracy, and competence of the solver, some tests have been taken using the parameters of regression, state transition, and error histogram.