## 1 Introduction  
Gas diffusion layers (GDLs) are porous transport layers that are used in fuel cells to allow the transport of fluids from the flow channels to the membrane. This requires good gas permeability in the through-plane (TP) direction. For transport under the ribs of a flow field, a good permeability in the in-plane (IP) direction is also desired. [PERSON] et al. [1] investigated the effect of GDL compression on the bypass of gases below the ribs of the flow field. A critical review of mesoscale modeling in electrochemical devices was presented by [PERSON] and [PERSON] [2]. [PERSON] et al. [3] evaluated the permeability of dry GDLs and several other properties of the micro-structure. The porosity and shape of the pores was investigated by [PERSON] et al. [4]. They used X-ray tomography for the analysis of structural properties of a GDL under compression. Meanwhile, [PERSON] et al. [5] reconstructed fiber-based GDLs, calculated the impact of compression on the micro-structure by means of the finite element method (FEM), and then analyzed the compressed GDL using computational fluid dynamics (CFD). The relevance of compression on the properties of GDLs was shown by [PERSON] et al. [6], who investigated the micro-structure of paper-type GDLs via X-ray tomography. They analyzed GDLs under ribs and under the channels of a flow field, and also analyzed them in fresh and aged states. Permeability and tortuosity were some of the features addressed in their studies. [PERSON] et al. [7] measured the TP and IP permeability of GDLs, with and without polytetrafluorethylene (PTFE), and with and without micro-porous layer (MPL). In order to reduce costs, GDLs from novel materials were developed by [PERSON] et al. [8]. [PERSON] et al. [9] analyzed GDL properties using Lattice-Boltzmann (LB) simulations based on reconstructed micro-structures. Extending to smaller scales and beyond permeability, [PERSON] et al. [10] developed LB-based methods to obtain further characteristics of porous materials even in nanoscale regions. They identified the relevance of several mechanism on the gas transport in porous materials. Artificial intelligence (AI) enters the picture when machine learning (ML) or the deep learning (DL) methods are applied in the field of electrochemical research. [PERSON] et al. [11] recently presented an overview of the application of ML in the field of fuel cell research, in particular for polymer electrolyte fuel cells (PEFCs). However, the use of AI methods is still growing and is beyond of the scope of this extensive review article. In previous investigations, the suitability of convolutional neural networks (CNNs) was demonstrated by [PERSON] et al. [12] for the prediction of the TP permeability of paper-type GDLs. The data were previously generated via LB simulations. In a similar manner, a CNN was developed by [PERSON] and [PERSON] [13], predicting the permeability of fibrous GDLs. In their study, they used the pore network modeling (PNM) method to generate their data. The accuracy of predicting the permeabilities of porous media using CNNs in the context of LB simulations was shown by [PERSON] et al. [14]. In turn, the relevance of investigations in the GDL micro-structure was pointed out by [PERSON] et al. [15], who summarized the mechanics, techniques, and modeling approaches for GDL degradation. Using similar ML methods, [PERSON] et al. [16] coupled their ML application with CFD and applied it to a variety of metal foams. [PERSON] et al. [17] investigated the impact of the number of hidden layers in a CNN on the accuracy of the prediction of permeability and velocity in sphere-based porous materials, e.g., sandstone. The data were generated using LB simulations. [PERSON] et al. [18] presented a CNN in the field of signal processing that is robust against noise added to datasets for prediction. The performance prediction of PEFCs by a neural network (NN) was coupled to flow channel optimization by [PERSON] et al. [19]. Applied to X-ray tomography, [PERSON] et al.