Currently I'm looking into possibilities to prove that a trained neural network (based on a regression task) performs well on the whole sample space based on statements made from test set metrics. In other words, I want to make sure that there is no sample or region of samples out there, where the network performs badly on, assuming that the sample space is finite.

Are there any well established approaches for this?