The overall procedure begins with comprehensive data preprocessing, including filtering the dataset for valid values, applying log transformations to the pressure and target variables, and scaling the data using computed means and standard deviations. The preprocessed data are then split into training and validation sets. The Inducing GP model is instantiated with the scaled training inputs and original unscaled data (used for the inverse transformations during IP initialization). Training proceeds by minimizing the NLL using the Adam optimizer over mini batches, with early stopping based on validation performance (measured via NLL) to prevent overfitting. Once the optimal hyperparameter configuration is determined, the model is retrained on the entire dataset (combining training and validation sets), and final performance is evaluated using metrics such as mean absolute error (MAE) and 𝑅² after reversing the scaling and log transformations.