  We present AnisoGNNs -- graph neural networks (GNNs) that generalize
predictions of anisotropic properties of polycrystals in arbitrary testing
directions without the need in excessive training data. To this end, we develop
GNNs with a physics-inspired combination of node attributes and aggregation
function. We demonstrate the excellent generalization capabilities of AnisoGNNs
in predicting anisotropic elastic and inelastic properties of two alloys.
