  Despite the success of neural networks at solving concrete physics problems,
their use as a general-purpose tool for scientific discovery is still in its
infancy. Here, we approach this problem by modelling a neural network
architecture after the human physical reasoning process, which has similarities
to representation learning. This allows us to make progress towards the
long-term goal of machine-assisted scientific discovery from experimental data
without making prior assumptions about the system. We apply this method to toy
examples and show that the network finds the physically relevant parameters,
exploits conservation laws to make predictions, and can help to gain conceptual
insights, e.g. Copernicus' conclusion that the solar system is heliocentric.
