I have a dataset from particle physics of 3D 'images' containing three spacial coordinates and one energy coordinate. I want to train a generative model for generation of a similar dataset. I am using GANs at the moment for this. An important pre-processing step for this is normalisation of the data between -1 and 1. If this was an image, I could have divided all the entries by 255 to achieve this since that's the maximum value that a pixel intensity can reach. However, in this case, there is no upper bound for the energy. I am not sure what to do in this case. Any suggestion is welcome.