  Digital image correlation (DIC) has become a valuable tool to monitor and
evaluate mechanical experiments of cracked specimen, but the automatic
detection of cracks is often difficult due to inherent noise and artefacts.
Machine learning models have been extremely successful in detecting crack paths
and crack tips using DIC-measured, interpolated full-field displacements as
input to a convolution-based segmentation model. Still, big data is needed to
train such models. However, scientific data is often scarce as experiments are
expensive and time-consuming. In this work, we present a method to directly
generate large amounts of artificial displacement data of cracked specimen
resembling real interpolated DIC displacements. The approach is based on
generative adversarial networks (GANs). During training, the discriminator
receives physical domain knowledge in the form of the derived von Mises
equivalent strain. We show that this physics-guided approach leads to improved
results in terms of visual quality of samples, sliced Wasserstein distance, and
geometry score when compared to a classical unguided GAN approach.
