## 1 Introduction  
The efficient operation of polymer electrolyte fuel cells (PEFs) requires knowledge of the properties of all of their components, as well as the proper configuration and design of the entire system. One component of interest is the gas diffusion layer (GDL), which is typically made from fibers. The relevance of the structural characteristics of porous media was shown by [PERSON] et al. [1], who visually depicted the deformation of fibrous GDLs under misaligned flow-field structures in a fuel cell assembly. Although the fibers are relevant for detailed investigations within the micro-structure of the fluid through-flow, macroscopic properties--e.g., permeability--are required for optimizations at higher scales (cells and stack), as was shown by [PERSON] et al. [2] and [PERSON] et al. [3]. Meanwhile, [PERSON] et al. [4] investigated the role of gas permeability (through-plane as well as in-plane) on fuel cell performance. Again, the macroscopic properties of porous media can be obtained from measurements or mesoscale transport simulations. [PERSON] et al. [5] presented a comprehensive overview of the properties of PEFC components, whereas [PERSON] et al. [6] focused on GDL properties. In turn, [PERSON] et al. [7] reported some variation in the diffusivities measured by different researchers. The electrochemical properties of GDLs were experimentally outlined by [PERSON] et al. [8]. Furthermore, these properties can be determined by means of transport simulations of the micro-structure. This was previously undertaken by the authors of [9] and fleece-type GDLs [10], as well as by other researchers [11]. [PERSON] et al. [12] simulated gas transport in woven cloth GDLs. Although the influence of the orientation of straight fibers on gas permeability was already investigated by [PERSON] and [PERSON] [13], this topic remained an issue in two-phase investigations by [PERSON] et al. [14]. All of these studies have in common the fact that they required extensive computational resources. Even more computational resources were employed by [PERSON] and [PERSON] [15], who simulated gas flow through a fiber-based GDL using lattice Boltzmann (LB) simulations with a high degree of accuracy, operating in a hierarchical octree-based mesh. Nowadays, methods based on artificial intelligence (AI) have been rising to the forefront of various fields of research. A prominent member of the AI portfolio is machine learning (ML). Furthermore, deep learning (DL) makes up part of the broader ML family and is based on artificial neural networks (ANNs) featuring non-trivial structures [16]. [PERSON] et al. [17] acknowledged the rising significance of ML for databases on material properties, identifying an ongoing lack in several fields, some of which include energy storage and conversion. Supervised learning--again, a form of ML--employs data sets, each of which are labeled with so-called features to train an ML model. Afterwards, the ML model can be used as a black box to obtain predictions based on previously unseen features in the data. The appropriate collection of training data is crucial for the efficiency of the ML model [16]. Data-driven approaches for predicting the material characteristics of micro-structures have been utilized by several research groups. [PERSON] et al. [18], for instance, coupled LB simulations to a pore network model (PNM) by means of ML, utilizing LB simulations on sections of the micro-structure of sandstone as the training data. [PERSON] et al. [19] demonstrated that the prediction of the permeability of sandstone by an ML model is possible with a small number of data-sets in the training phase. [PERSON] et al. [20] developed a DL model in order to predict the permeability of petrochemical rocks, generating the permeability of their training samples using volume of fluid (VOF) simulations. The porosity, permeability, and tortuosity were obtained from black/white (BW) images of porous media by [PERSON] and [PERSON] [21], who analyzed image series across a wide range of these three features.