Discovering physical concepts with neural networks
This work addresses the challenge of machine-assisted scientific discovery for researchers, though it is incremental as it focuses on toy examples.
The paper tackles the problem of using neural networks for general-purpose scientific discovery from experimental data without prior assumptions, and demonstrates that their method can identify physically relevant parameters, exploit conservation laws, and provide conceptual insights like heliocentrism in toy examples.
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.