Machine learning approaches are increasingly employed to predict a variety of physical properties, accelerating and expanding access to material data. However, many of those physical properties adhere to inherent constraints, such as symmetry relationships or limits. In the case of tensorial properties, each eigenvalue may be predicted as an independent scalar, however, such treatment effectively ignores the underlying symmetry information of the tensor.