Year: 2012
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Hillar, Christopher, Sommer, Friedrich
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2095883102