Wajid Ullah Jan

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About

Wajid Ullah Jan is a rising researcher whose work sits at the intersection of fluid dynamics, magnetohydrodynamics (MHD), and artificial intelligence. His primary research focus involves applying advanced neural network techniques to solve complex problems in nanofluid flow and heat transfer. His most cited paper, "A neural networks technique for analysis of MHD nano-fluid flow over a rotating disk with heat generation/absorption" (2024), has already garnered 5 citations, signaling early impact in the field. In this work, Jan introduced a novel neural network backpropagation Levenberg-Marquardt scheme (NNB-LMS) to numerically model the behavior of MHD nanofluid flow over a rotating disk—a problem with significant applications in thermal engineering and energy systems. By demonstrating convergent stability and accurate numerical solutions for heat generation and absorption effects, his contribution bridges the gap between machine learning and classical fluid mechanics. This innovative approach offers a powerful computational tool for researchers tackling nonlinear, multi-physics flow problems. Jan’s work positions him as a promising voice in the growing field of AI-driven computational fluid dynamics, with potential to influence both theoretical modeling and practical engineering design.