  This paper uses the peridynamic theory, which is well-suited to crack
studies, to predict the crack patterns in a moving disk and classify them
according to the modes and finally perform regression analysis. In that way,
the crack patterns are obtained according to each mode by Molecular Dynamic
(MD) simulation using the peridynamics. Image classification and regression
studies are conducted through Convolutional Neural Networks (CNNs) and the
neural processes. First, we increased the amount and quality of the data using
peridynamics, which can theoretically compensate for the problems of the finite
element method (FEM) in generating crack pattern images. Second, we did the
case study for the PMB, LPS, and VES models that were obtained using the
peridynamic theory. Case studies were performed to classify the images using
CNNs and determine the PMB, LBS, and VES models' suitability. Finally, we
performed the regression analysis for the images of the crack patterns with
neural processes to predict the crack patterns. In the regression problem, by
representing the results of the variance according to the epochs, it can be
confirmed that the result of the variance is decreased by increasing the epoch
numbers through the neural processes. The most critical point of this study is
that the neural processes make an accurate prediction even if there are missing
or insufficient training data.
