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Learning Parameters of dRGT Black Holes in Nonlinear Electrodynamics with Neural Network Scenarios
Adil Belhaj, Salah Eddine Ennadifi, Maryem Jemri
Using machine learning methods, we present constraining parameter scenarios of AdS black holes in dRGT-like massive gravity coupled to nonlinear electrodynamics. We first approach…
Constraining Black Hole Parameters in Non-Commutative Geometry using Machine Learning
Maryem Jemri
Motivated by string theory, we constrain non-commutative black hole parameters through shadow behaviors using machine learning techniques combined by CUDA computations. To do so, w…
Constraining Black Hole Shadows in Dunkl Spacetime using CUDA Numerical Computations
Saad Eddine Baddis, Adil Belhaj, Hajar Belmahi +1
With the help of CUDA high-performance numerical codes exploited in machine learning, we investigate the shadow aspect of new rotating and charged black holes using the Dunkl deriv…
Stability and Criticality Behaviors of Accelerating Charged AdS Black Holes in Rainbow Gravity
Hajar Belmahi, Maryem Jemri, Rajaa Salih
In this work, we investigate the thermodynamical properties of accelerated charged Anti-de Sitter black holes in the context of rainbow gravity. Concretely, we compute the correspo…