Fig. 1

Pipeline of the deep learning-based sub-surface morphology transformation simulation. The binarized SEM images of GON cross-sections are used as training data, and the deep learning architecture is trained to encode the images into single latent variable and reconstruct it into original images. The pink segment of the entire model is initially trained for parameter training, followed by selective training of green segment for the convolutional reconstruction of original training images