Table represents the comparison between the GDL functional properties and the contact resistance taken from the deterministic learning and the physics-based calculations at different optimal scenarios. Scenarios 𝐶 𝑓 2 , 𝐶 𝑓 3 , and 𝐶 𝑓 4 exhibited a percent error of less than 10 %, except for the contact resistance which showed errors of less than 12% and Electrical TP of less than 13 %. On the other hand, 𝐶 𝑓 1 demonstrated a percent error of 37 % for the electrical TP, predicting a value of 155 S m -1 . As shown in figure , which illustrates the predictive capability of deterministic learning for the functional property Electrical TP, there is a lack of data points in the region beyond 70 S m -1 . Consequently, the accuracy of predictions in this high-value range is limited. However, improvements in predictive accuracy are evident in other scenarios. Furthermore, 𝐶 𝑓 1 showed slightly higher percent error of 40 % compared to the 1000 µm x 1000 µm domain size. In general, there is a consistency between the values calculated from the physicsbased model and the values predicted by the deterministic learning, showing that the deterministic learning integrated within our optimization framework is able to predict GDL functional properties, and the contact resistance between the GDL and the MPL, correctly.