DescriptionPhysics-informed generative adversarial networks (PI-GANs) are used to learn the underlying probability distributions of spatially-varying material properties (e.g., microstructure variability in a polycrystalline material). While standard GANs rely solely on data for training, PI-GANs encode physics in the form of stochastic differential equations using automatic differentiation. The goal here is to show that experimental data from a limited number of material tests can be used with PI-GANs to enable unlimited virtual testing for aerospace applications. Preliminary results using synthetically generated data are provided to demonstrate the proposed framework. Deep learning and automatic differentiation capabilities in Tensorflow were implemented on Nvidia Tesla V100 GPUs.