Another option is the digitalization of experimental electrode microstructures through computer tomography. In this case the fidelity to the experiments is high, but it is resource intensive and, although the active material can generally be properly accounted for, the binder and carbon black (CB) are rarely well captured by this this approach. Third, Convolutional Neural Network-based generative methods exist that allow the generation of 3D microstructures from single images, as well as other deep learning-based tools trained on physics-based simulation results, but they require significant amount of training data, and they lack a description of physical processes occurring during electrode fabrication. The last option is the stochastic generation of electrode microstructures. This method is based on the generation of a backbone of the active material (AM) particles, which is then completed with a phase known as carbon binder domain (CBD) consisting of a combination of the CB and binder. The advantage of this method is its speed and parameter flexibility, which allows exploring a wide range of microstructural parameters. However, it is not able to correlate output with a particular set of manufacturing parameters, such as drying rate and calendering pressure.