期刊
PHYSICAL REVIEW LETTERS
卷 124, 期 1, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.124.010508
关键词
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资金
- Swiss National Science Foundation through SNSF Project [200020_165843]
- Swiss National Supercomputing Centre (CSCS) [da04]
- National Centre of Competence in Research Quantum Science and Technology (QSIT)
- FQXi grant Physics of the observer
- ETH Zurich
- ETH Foundation through the Excellence Scholarship & Opportunity Programme
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 modeling 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.
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