Sensitivity analysis and modeling of ternary hybrid nanofluid flow in rotating annulus using a hybrid physics-informed neural network

Researchers have developed a hybrid physics-informed neural network (PINN) and artificial neural network (ANN) model to analyze ternary hybrid nanofluid flow in rotating machinery. The study demonstrates high-accuracy predictions for heat transfer rates, offering a solution to convergence issues found in traditional numerical methods.