  In the article "Distilling free-form natural laws from experimental data",
Schmidt and Lipson introduced the idea that free-form natural laws can be
learned from experimental measurements in a physical system using symbolic
(genetic) regression algorithms. An important claim in this work is that the
algorithm finds laws in data without having incorporated any prior knowledge of
physics. Upon close inspection, however, we show that their method implicitly
incorporates Hamilton's equations of motions and Newton's second law,
demystifying how they are able to find Hamiltonians and special classes of
Lagrangians from data.
