There are also some other advanced machine-learning techniques available that can better evaluate accuracy of an ANN model. For example, using the Kfold cross validation method [31] ensures that every observation from the original dataset has the chance of appearing in training and test sets, especially when limited input **data** is available. In our examples for lattice optimization, rather than using the time-consuming cross-validation that K-fold requires, sufficient training **data** were generated with a particle tracking simulation code, because using only one-turn tracking for such rings requires much less of a demand on computational resources.