  Neural networks have become popular in many fields of science since they
serve as promising, reliable and powerful tools. In this work, we study the
effect of data augmentation on the predictive power of neural network models
for nuclear physics data. We present two different data augmentation
techniques, and we conduct a detailed analysis in terms of different depths,
optimizers, activation functions and random seed values to show the success and
robustness of the model. Using the experimental uncertainties for data
augmentation for the first time, the size of the training data set is
artificially boosted and the changes in the root-mean-square error between the
model predictions on the test set and the experimental data are investigated.
Our results show that the data augmentation decreases the prediction errors,
stabilizes the model and prevents overfitting. The extrapolation capabilities
of the MLP models are also tested for newly measured nuclei in AME2020 mass
table, and it is shown that the predictions are significantly improved by using
data augmentation.
