Discovering invariants via machine learning
Invariants and conservation laws convey critical information about the underlying dynamics of a system, yet it is generally infeasible to find them from large-scale data without any prior knowledge or human insight.We propose ConservNet to achieve this goal, a neural network that spontaneously discovers a conserved quantity from grouped data where