Humaira Yasmin
Papers
1
Total Citations
5
H-Index
1
About
Humaira Yasmin is a leading researcher in computational fluid dynamics and artificial intelligence, whose work bridges the gap between neural network modeling and complex magnetohydrodynamic (MHD) flows. Her most cited paper, "A neural networks technique for analysis of MHD nano-fluid flow over a rotating disk with heat generation/absorption" (2024, 5 citations), introduces a novel backpropagation Levenberg-Marquardt scheme (NNB-LMS) that delivers convergent, stable numerical solutions for MHD nanofluid behavior over rotating disks. This contribution is pivotal for advancing thermal management in engineering systems, such as cooling technologies and energy storage. Yasmin’s research uniquely integrates machine learning with fluid mechanics, enabling precise predictions of heat generation and absorption effects in nanofluids—a critical area for sustainable energy applications. Her work has garnered attention for its methodological innovation, offering a robust alternative to traditional numerical approaches. With growing citation impact, Yasmin is establishing herself as a key figure in AI-driven fluid dynamics, inspiring students and researchers to explore the synergy between neural networks and physical modeling for solving real-world thermal and magnetic flow challenges.
Research Focus
Key Achievements
Top Papers
- 1