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This paper concerns the use of a priori information on the symmetry of cross differentials available for problems that seek to approximate the gradient of a differentiable function. We derive the appropriate network constraints to incorporate the symmetry information, show that the constraints do not reduce the universal approximation capabilities of feedforward networks, and demonstrate how the constraints can improve generalization.