Recent experiments using non-destructive 3D characterization has shown that our understanding of grain growth mechanisms is incomplete. Grain boundary (GB) migration is being driven not just to reduce the GB area but to also reduce the GB energy via GB reorientation and replacement. These mechanisms are related to the impact of a GB’s inclination on its energy. In this work we use modeling and simulation together with 3D non-destructive characterization of grain growth experiments to understand the impact of GB inclination on grain growth behavior. We start by illustrating how inclination dependence is added to a Monte Carlo Potts model, a new mode filter model we have developed, and in machine learning models of grain growth. We then use models of inclination-dependent grain growth to help interpret results shown in grain growth experiments of textured alumina slip cast in an applied magnetic field. We end by using models of inclination- and misorientation-dependent grain growth to understand GB migration opposite the direction of the GB curvature that has been observed in grain growth of SrTiO3.