  The influence of the microstructure of a polycrystalline material on its
macroscopic deformation response is still one of the major problems in
materials engineering. For materials characterized by elastic-plastic
deformation responses, predictive computational models to characterize
crystal-plasticity (CP) have been developed. However, due to their large demand
of computational resources, CP simulations cannot be straightforwardly
implemented in hierarchical computational models such as FE$^{2}$. This
bottleneck intensifies the need for the development of macroscopic simulation
tools that can be directly informed by microstructural quantities. Using a 3D
Finite-Element solver for CP, we generate a macroscopic yield function database
based on general loading conditions and crystallographic texture. We
furthermore assume an independence of the yield function to hydrostatic
pressure of the yield function. Leveraging the advancement in statistical
modeling we describe and apply a machine learning framework for predicting
macroscopic yield as a function of crystallographic texture. The convexity of
the data-driven yield function is guaranteed by using partially input convex
neural networks as the predictive tool. Furthermore, in order to allow for the
predicted yield function to be directly incorporated in time-integration
schemes, as needed for the Finite Element method, the yield surfaces are
interpreted as the boundaries of signed distance function level sets.
