The entire data set was randomly divided into three distinct subsets: training (90%), validation (5%), and test (5%). The training set is used only for fitting purposes, while the validation set plays a crucial role in monitoring the training process. Additionally, the test set is used as an independent assessment of the fitting quality. To prevent overfitting in the NN training, the "early stopping" approach is used by halting the training process when the performance on the validation set starts to degrade.