The distributions in each dataset of the geometrical tortuosity are shown in Figure , which is the target predicted by the NN in this section. The geometric tortuosity of the GDL is an important property as it plays a crucial role in gases, liquids and heat transport within the system. It also affects the permeability, which ultimately impacts the efficiency and performance of the PEMFC. Higher tortuosity means longer diffusive paths, which will affect the GDL's transport properties. It can be highlighted that both GDL200 and GDL1000 geometrical tortuosity are right-skewed distributions, and GDL1000 is shifted to the right of the GDL200 one. While the mean value of the GDL200 is 1.07, the GDL1000 is 1.09. Also, the minimum value in GDL200 is 1.03, while in GDL1000 is 1.08. Both of the distributions extend till 1.17. where the loss decreases with an asymptotic behavior and reaches a plateau close to 2%. When this NN is evaluated in the test sets, we have a MAPE value of 2.13% for GDL200 and of 2.61% for GDL1000. This higher error for GDL1000 can be improved by applying the TL approach that already showed improvement in with the experimental dataset. By adding and training the extra layer to the pre-trained NN, then the evaluation in the GDL1000 test set decreases to 1.96%. This information is summarized in Table , demonstrating that there is also an improvement with the TL approach when applied to this stochastically generated dataset. Individual predictions are plotted versus the target values for the GDL1000 test set and both pre-trained (red squares) and TL approach (blue circles) NNs in Figure . As expected with the values of Table , the predictions of the pre-trained NN have a higher scatter in the predicted y-axis. This scatter is improved with the TL approach NN by having each prediction closer to the ideal prediction line. This improvement is clearer in the range that goes from 1.08 to 1.12, the three cases with higher geometrical tortuosity are worsened. However, these values are in the tail of the distribution, being less sampled than the ones in the range that is improved. Finally, we also use XAI in these datasets where entries of the GDL200 and GDL1000 test sets are randomly selected and evaluated with the pre-trained and the TL approach NNs, respectively. The obtained Shapley values are displayed in Figure . In the first case we have a major influence in the negative direction, relative to the base value, that it is mandated by thickness, fiber concentration and fiber diameter.