Data-efficient deep neural surrogates for simulating rotating non-Newtonian convective flows in anisotropic porous media under convective boundary conditions
Published online on June 17, 2026
Abstract
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Ahead of Print.
This study develops a novel Physics-Informed Neural Network (PINN) framework for coupled flow and heat transfer in a rotating channel containing a power-law non-Newtonian fluid within an anisotropic porous medium. The proposed approach uniquely integrates ...
This study develops a novel Physics-Informed Neural Network (PINN) framework for coupled flow and heat transfer in a rotating channel containing a power-law non-Newtonian fluid within an anisotropic porous medium. The proposed approach uniquely integrates ...