20 citations · 37 across the 13 of their papers we have counts for
5 papers · 1 filter
Towards a Machine Learning Solution for Hubble Tension: Physics-Informed Neural Network (PINN) Analysis of Tsallis Holographic Dark Energy in Presence of Neutrinos
Muhammad Yarahmadi, Amin Salehi
We present a Physics-Informed Neural Network (PINN) framework for reconstructing the redshift-dependent Hubble parameter \(H(z)\) within the Tsallis Holographic Dark Energy (THDE)…
A Bayesian PINN Framework for Barrow-Tsallis Holographic Dark Energy with Neutrinos: Toward a Resolution of the Hubble Tension
Muhammad Yarahmadi, Amin Salehi
We investigate the Barrow-Tsallis Holographic Dark Energy (BTHDE) model using both traditional Markov Chain Monte Carlo (MCMC) methods and a Bayesian Physics-Informed Neural Networ…
Cosmic Bulk Flow Analysis in Modified Gravity Theories: and Perturbed Models with Neutrino Coupling
Muhammad Yarahmadi, Amin Salehi
In this study, we explore the characteristics of bulk flow across various redshift ranges within the frameworks of gravity, perturbed gravity, and perturbed gr…
Using the Kaniadakis horizon entropy in the presence of neutrinos to alleviate the Hubble and Tensions
Muhammad Yarahmadi, Amin Salehi
The tension stands as a prominent challenge in cosmology, serving as a primary driver for exploring alternative models of dark energy. Another tension arises from measureme…
Comparative Analysis of Perturbed Gravity and Perturbed Rastall Gravity Models in Describing Cosmic Evolution from Early to Late Universe Relative to the CDM Model
Muhammad Yarahmadi, Amin Salehi, Hadis Mousavi
This study conducts a meticulous examination of the cosmological implications inherent in Rastall gravity and gravity models, assessing their efficacy across distinct cosmic…