The second application of the simple TL approach was in the context of PEMFCs with stochastically generated GDL datasets with different volume sizes. In this case, small volume microstructures with lots of calculations were used to train a NN to predict the geometric tortuosity considering as input parameters the fiber diameter, the fiber concentration, the binder concentration, the thickness, and the compression. Then, the NN was transferred to a smaller dataset with microstructures of larger volume that were computationally more expensive to characterize. Since these datasets were stochastically generated, they have a more homogeneous distribution than the experimental ones. This property in the data resulted in even better results, which proved that the simple TL approach can perform better when applied to better distributed datasets.