Physics-guided DIC-GAN: Generating artificial displacement data of cracked specimen

Training data along with trained generative AI models used in the research article "Generating artificial displacement data of cracked specimen using physics-guided adversarial networks" by Melching et al. 2024 published in Machine Learning: Science and Technology (https://iopscience.iop.org/article/10.1088/2632-2153/ad15b2) The data consists of interpolated planar displacement fields of cracked specimen obtained by digital image correlation during fatigue crack growth experiments conducted at the Institute of Materials Research of the German Aerospace Center (DLR) in Cologne, Germany. The data is described in more detail in the mentioned journal article.