[Valectar]

We don't have the rules that we want the neural network to learn, if we did we could just directly use those rules, and there would be no need for ML to solve the problem. In this case we want the ML to learn how to infer spatial information from two dimensional images, and the process that generates the data it trains on cannot do this at all. It can create two dimensional images from spatial information, which is a much simpler and effectively solved process.

There are cases when we want a machine learning model to do the same thing as the process which generate it's data, like in the case of model's learning to replicate physics simulations, but even then the entire point is for the machine learning model to accomplish the same or a similar result but in a more computationally efficient way.