The datasets are training sets (file names ending with _train.csv) and validation sets (file names ending with _validate.csv). The machine learning algorithm is trained on the data in a _train.csv file and validated against the data in the corresponding _validate.csv file. The first column of each file is the input variable. The second column is the available values for the intermediate target variable. The last column of each file is the values of the final target variable.

The extrapolation_using_Y training and validation sets are used to validate the ability of the machine learning algorithm to perform extrapolation using intermediate target variable. The sinosoidal_noise training and validation sets are used to validate the ability of the machine learning algorithm to perform extrapolation using uncertainty in the intermediate target variable. The phase_transition and droplet_diffraction training and validation files contain the experimental data for the real-world physical examples that we applied the methodology to.