Yousef Jawarneh
Papers
2
Total Citations
6
H-Index
1
About
Yousef Jawarneh is a rising researcher at the forefront of computational fluid dynamics and artificial intelligence, specializing in the magnetohydrodynamic (MHD) flow of nanofluids. His work uniquely bridges classical fluid mechanics with modern machine learning, developing intelligent neural network models to solve complex, nonlinear flow problems. His most impactful contribution is the novel application of the backpropagation Levenberg-Marquardt scheme (NNB-LMS) to analyze MHD nanofluid flow over a rotating disk with heat generation and absorption—a study that has already garnered 5 citations since its 2024 publication. This work demonstrates convergent stability and provides a robust numerical framework for thermal management systems. In 2025, Jawarneh extended this methodology to investigate the effects of velocity slip conditions on viscous nanofluid flow, further validating the power of neural networks in capturing intricate boundary phenomena. By replacing traditional numerical solvers with intelligent, data-driven algorithms, Jawarneh is pioneering a new paradigm for solving MHD problems, offering faster and more adaptable solutions for engineering applications in energy systems and advanced manufacturing.
Research Focus
Key Achievements
Top Papers
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