Discovering physical concepts with neural networks

Understand

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.