As explained in the previous sections, this network design leverages TensorFlow and TensorFlow Probability to create a model capable of probabilistic predictions. It starts with defining a custom loss function, the negative log-likelihood, which calculates how well the predicted probability distributions match the true values, thus guiding the model to improve its probabilistic accuracy. The model is constructed using the Sequential API, beginning with an input layer tailored to match the shape of the training data. For this network to function correctly, the training dataset must be three-dimensional, with the shape (number of samples, 1, features). This ensures that the input layer can properly process the data, which typically involves time-series or sequential data. In detail, Figure shows the sensitivity of the calendar capacity loss against the features used in the training process. As can be seen in this figure, we used time as one of the features for the calendar aging training because of using acceleration in the laboratory static conditions. The violin plot underscores temperature as the factor with the most significant influence on battery capacity loss during calendar aging. While time shows negligible impact, and SOC displays moderate influence. Following the input layer, the architecture includes two dense layers. The first dense layer consists of 512 neurons with ReLU activation functions and He normal initialization to capture complex patterns in the data. The second dense layer, with 256 neurons, continues this pattern of transformation. A crucial aspect of this design is the probabilistic output layer. Here, a dense layer generates the necessary parameters for a normal distribution, which are then utilized by a TensorFlow Probability `IndependentNormal` layer. This layer outputs a normal distribution, allowing the model to predict not just single values but distributions, thereby quantifying prediction uncertainty. The model is compiled using the Adam optimizer with a learning rate of 10 -4 , paired with the negative log-likelihood loss function to ensure the training process focuses on improving the accuracy of the probabilistic predictions.