The overall performance of the proposed TL approach might not be perfect, considering the percentage errors for some cases presented in Table . Nevertheless, this could be attributed to the particular distribution of the pre-existing experimental dataset. That is where our stochastically generated GDL manufacturing dataset comes into the picture. The source GDL200 dataset has 240 entries, while the target GDL1000 has 78 entries. These sizes maintain a similar relationship between source and target datasets sizes of the experimental demonstration. But, since these are stochastically generated, they have a more homogeneous distribution. Despite the improvement in this aspect, these data were not specifically generated for the purpose of demonstrating this TL approach, which makes the results obtained even more robust. The distribution of the input features for the NN to predict the geometric tortuosity are shown in Figure . In the GDL200 dataset, a uniform distribution is presented. While the GDL1000 presents some particularities due to the smaller amount of data. For both datasets we have similar ranges for every input parameter. Fiber diameter ranges from 6.0 to 12.0 µm. Fiber and binder concentrations vary from 10% to 25% and from 5% to 10%, respectively. The thickness goes from 280 to 320 µm, and, finally, the factor compression from 0.0 to 0.5.