Ajed Akbar
Papers
3
Total Citations
83
H-Index
2
About
Ajed Akbar is a rising computational scientist whose research sits at the intersection of nanofluid dynamics, magnetohydrodynamics (MHD), and artificial intelligence. His primary focus is on developing intelligent neural network frameworks—specifically Levenberg-Marquardt backpropagation schemes—to solve complex, nonlinear fluid flow problems that are analytically intractable. Akbar’s most impactful work, “Intelligent computing paradigm for the Buongiorno model of nanofluid flow with partial slip and MHD effects over a rotating disk” (2022), has already garnered 77 citations, establishing a benchmark for using AI to model heat and mass transfer in rotating systems with slip conditions. He has further advanced this methodology by incorporating heat generation and absorption effects (2024) and velocity slip conditions (2025), consistently demonstrating the stability and convergence of neural network solvers for MHD viscous nanofluid flows. By replacing traditional numerical solvers with trained neural networks, Akbar’s contributions offer faster, more adaptable solutions for engineering applications such as thermal management and rotating machinery. His growing body of work signals a significant shift toward data-driven approaches in theoretical fluid mechanics, making him a key figure to watch in computational nanofluid research.
Research Focus
Key Achievements
Top Papers
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