A Heuristic Physics-Informed Neural Network for Unsteady Magnetohydrodynamic Squeezing Flow through a Darcy-Forchheimer Porous Channel with Brownian Motion, Thermophoresis, Thermal Radiation, and Heat Generation
DOI:
https://doi.org/10.66972/iscis202617Keywords:
Hybrid Nanofluid Squeezing Flow, Darcy-Forchheimer Magnetohydrodynamics, Buongiorno Nanoparticle Transport, Physics-Informed Neural Network, Green Function Fixed Point Analysis, Particle Swarm and Neural Network AlgorithmAbstract
The unsteady two-dimensional magnetohydrodynamic squeezing flow of an electrically conducting hybrid nanofluid through a Darcy-Forchheimer porous channel is studied under the Buongiorno Brownian and thermophoretic fluxes, nonlinear Rosseland radiation, and a temperature-dependent heat source or sink. A symbolically confirmed similarity transformation reduces the governing partial differential equations to a coupled nonlinear ordinary differential system, which is recast as a fixed point problem through explicit Green functions. Existence, uniqueness, and an a posteriori error certificate are established. The fields are then obtained by a heuristic physics-informed neural network trained by a hybrid particle swarm and neural network algorithm with a trust-region refinement. The influence of every physical parameter on the velocity, temperature, and concentration is analysed.
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