  The rate of fatigue crack growth in Nickle superalloys is a critical factor
of safety in the aerospace industry. A machine learning approach is chosen to
predict the fatigue crack growth rate as a function of the material
composition, material properties and environmental conditions. Random forests
and neural network frameworks are used to develop two different models and
compare the two results. Both the frameworks give good predictions with $r^2$
of 0.9687 for random forest and 0.9831 for neural network.
