  Speckle patterns produced by coherent X-ray have a close relationship with
the internal structure of materials but quantitative inversion of the
relationship to determine structure from speckle patterns is challenging. Here,
we investigate the link between coherent X-ray speckle patterns and sample
structures using a model 2D disk system and explore the ability of machine
learning to learn aspects of the relationship. Specifically, we train a deep
neural network to classify the coherent X-ray speckle patterns according to the
disk number density in the corresponding structure. It is demonstrated that the
classification system is accurate for both non-disperse and disperse size
distributions.
