  Multiscale simulations are demanding in terms of computational resources. In
the context of continuum micromechanics, the multiscale problem arises from the
need of inferring macroscopic material parameters from the microscale. If the
underlying microstructure is explicitly given by means of microCT-scans,
convolutional neural networks can be used to learn the microstructure-property
mapping, which is usually obtained from computational homogenization. The CNN
approach provides a significant speedup, especially in the context of
heterogeneous or functionally graded materials. Another application is
uncertainty quantification, where many expansive evaluations are required.
However, one bottleneck of this approach is the large number of training
microstructures needed. This work closes this gap by proposing a generative
adversarial network tailored towards three-dimensional microstructure
generation. The lightweight algorithm is able to learn the underlying
properties of the material from a single microCT-scan without the need of
explicit descriptors. During prediction time, the network can produce unique
three-dimensional microstructures with the same properties of the original data
in a fraction of seconds and at consistently high quality.
