TY - JOUR
TI - Neural networks paradigm for rotating flow of Jeffrey fluid with heat transfer between two parallel horizontal plates
AU - Mahariq Ibrahim
AU - Islam Saeed
AU - Ali Ishtiaq
AU - Fiza Mehreen
AU - Akbar Ajed
AU - Abas Syed Arshad
AU - Ullah Hakeem
AU - Akgul Ali
JN - Thermal Science
PY - 2025
VL - 29
IS - 5
SP - 3681
EP - 3696
PT - Article
AB - The current research explores the analysis of heat transfer in the rotating  Jeffrey fluid between two parallel plates, employing the BLMS-ANN approach  based on the back propagation Levenberg-Marquardt scheme. The fluid-flow is  initially described by a system of PDE, which is subsequently transformed  into a system of ODE through appropriate correspondence transformations and  boundary conditions. Eventually, the equations are rendered dimensionless  using a boundary-layer approximation. By changing parameters including the  radiation parameter, Rd, Deborah number, λ, viscosity parameter, R, Prandtl  number, Reynolds number, and Rotation parameter, kr, using the differential  transform method, a group of data for suggested BLMS-ANN is constructed for  several cases. To assess the predicted outcomes for specific scenarios, the  BLMS-ANN methodology undergoes testing validation and training.  Subsequently, the proposed model is scrutinized for confirmation. The  validity of the suggested BLMS-ANN approach is confirmed through regression  analysis, examination of mean square error, and histogram studies. The  suggested method differs from both the proposed and reference results with  an accuracy level between.