Besides experimental efforts to examine the impact of manufacturing parameters on electrode performance, mesoscale computational modeling has arisen as a valuable complementary tool to study how manufacturing parameters impact the electrode microstructure. Several pioneering tools supported on this mesoscale modeling have been previously proposed by us, providing insights in the impact of manufacturing parameters on the cell electrochemical performance and enabling the optimization of the fabrication process to diminish the manufacturing cost and the environmental effects. While slurry and drying stages are equally crucial to the final electrode microstructure, calendering has a more direct influence since it is the last stage. Therefore, the study of the calendering process is important to understand the performance of the fabricated electrode. In that line, besides experimental efforts, computational methodologies have been proposed and used. Additionally, a computational approach allows researchers to investigate a wide diversity of electrode materials, compositions, initial thicknesses, and so on, without the need for physical experiments, saving resources, time and reducing the risk of potential errors. In the context of our ARTISTIC project initiative, we have demonstrated how innovative experimentally validated physics-based simulations of the manufacturing process, including calendering, are beneficial in understanding the complexity among the different manufacturing process steps and their interrelationships in order to fabricate optimized electrodes. During the calendering process, important properties and phenomena, such as elasticity, plasticity, and spring-back recovery, play a crucial role directly influencing the final electrode performance. For example, due to applied stress, electrode undergoes permanent (plastic deformation) and reversible (elastic deformation) changes affecting its porosity, density and thickness. Similar to plastic deformation, spring-back effects are also studied extensively from an experimental viewpoint to understand the relationship between compaction process and performance in order to manufacture optimized electrodes. In parallel to experimental studies, computational methods such as the Discrete Element Method (DEM) have been applied to understand calendering effects on final electrode properties. Furthermore, the development of high-performance computing (HPC), for instance, supercomputers and cloud computing services, coupled with a sub-field of Artificial Intelligence (AI) such as Deep Learning (DL), are resources that can be used to accelerate the battery manufacturing process optimization as a whole. For example, Shodiev et al., in our group, predicted electrolyte flow infiltration over time by using a multi-layers perceptron (MLP) approach. Another study conducted by Duquesnoy et al. in our group, developed a data-driven fast framework to forecast electrode slurries rheological properties simulated by coarse grained molecular dynamics. Similarly, Convolutional neural networks (CNNs) have emerged as a primarily DL approach when dealing with multidimensional data input, such as the ones arising from image processing, taking advantage of the handling of input data topology. CNNs are made up of three major types of layers, namely convolutional layer, pooling layer, and fully-connected layer. The convolutional layer comprises filters, also called kernels, used to capture the specific features from the input data. Thus, CNNs are built by stacking multiple convolutional layers to extract low-level to high-level patterns or features. Pooling layers, which usually come after convolutional layers, are used to decrease the data dimensions, thus reducing the computational cost. There are three main types of pooling layers: max, average and mixed pooling, and can be used depending on the requirement of the study. The fully connected layers are used to connect neurons between two different layers, as well as process the flattened features in order to implement the final tasks, for example, classification or regression problems. The number of neurons in fully connected layers depends on the complexity of the problem. CNN architecture follows a hierarchical structure, allowing the extraction of higher-level features obtaining lower-level ones by considering proximal spatial correlations. Therefore, CNN are suitable to deal with time-series datasets, generally 1D grid topology, or imagery data sets which can be a 2D or 3D grid of pixels. Marcato et al., within our group, used the results of a 3D-resolved heterogeneous electrochemical model to train a CNN, that was then used to simulate with 3D resolution, the spatiotemporal lithiations heterogeneities of a NMC111 electrode (in a half-cell configuration) as well as its corresponding galvanostatic discharge curve. In addition, CNN, when coupled with MLP or Long Short-Term Memory (LSTM), makes it possible to perform time-series forecasting, which is one of the bases of the study presented in this article. Such combinations also reduce the complexity of training a DL model. As an example of 3D structure prediction, Pierson et al. employed a CNN-based model to predict microstructure-sensitive evolution of 3D cracking paths in a polycrystalline alloy. Additionally, Yang et al. present a DL approach combining CNN and LSTM to predict voltage discharge curves of 3D AM particle microstructures. Besides time-series studies, another approach is the generation of 3D image data. For instance, Kench et al. developed a model using Generative adversarial networks (GANs) trained with tomography and/or Scanning Electron Microscopy images to generate static 3D samples given some microstructural metrics. However, we believe that the development of a physics-informed model, like the one presented by us in this article, able to generate not only the final electrode microstructure, but also to capture the particle rearrangement dynamics occurring during the whole manufacturing process, is necessary to optimize the influence of manufacturing parameters on the electrode architecture.