  We construct neural networks that work for any Lie group and maintain gauge
covariance, enabling smooth, invertible gauge field transformations. We
implement these transformations for 4D SU(3) lattice gauge fields and explore
their use in HMC. We focus on developing loss functions and optimizing the
transformations. We show the effects on HMC's molecular dynamics and discuss
the scalability of the approach.
