Work

A machine learning neural network architecture for the accelerating universe based modified gravity

Year: 2025

Type: article

Abstract: 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.

Source: Egyptian Informatics Journal

DOI: 10.1016/j.eij.2025.100635

Authors: Zulqurnain Sabir, Basma Souayeh, Zahraa Zaiour, Alyn Nazal, Mir Waqas Alam, Huda Alfannakh

Institutions: Lebanese American University, King Faisal University

Language: English

Topic: Cosmology and Gravitation Theories

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funders: King Faisal University, Deanship of Scientific Research, King Saud University