The model is optimized by minimizing a mean square error (MSE) cost function using the Adam optimizer since it is one of the most stable gradient descent optimizers. The CNN model is implemented using the Python libraries, Tensorflow and Keras, within the 3.9 version of Python. The data set has been divided randomly into training (24) and testing datasets (4). Then, the training dataset is divided randomly into training and validation in a ratio of 90 to 10, respectively. The validation set is used to monitor convergence of the model during the training process. As opposed to the validation set, the test set is completely withheld from the training process. We believe that the total data set composed of 28 simulations is good enough for CNN to learn recognized patterns when dealing with time-series data, as shown in our previous work dealing with 3D-resolved electrochemical simulation data. Figure . Architecture of the 1D-CNN employed for our time-series electrode calendering simulation. The network consists of the following layers: input layer, hidden layers (convolution, batch normalization, max-pooling, dropout layers and fully connected layers) and output layer.