Automatic discovery of natural laws from experimental data

From the paper Michael Schmidt, Hod Lipson, "Distilling Free-Form Natural Laws from Experimental Data," Science 324: 81-85, 3 April 2009 .

The authors have demonstrated the discovery of physical laws, from scratch, directly from experimentally captured data with the use of a computational search. We used the presented approach to detect nonlinear energy conservation laws, Newtonian force laws, geometric invariants, and system manifolds in various synthetic and physically implemented systems without prior knowledge about physics, kinematics, or geometry. The concise analytical expressions that we found are amenable to human interpretation and help to reveal the physics underlying the observed phenomenon. Many applications exist for this approach, in fields ranging from systems biology to cosmology, where theoretical gaps exist despite abundance in data. Might this process diminish the role of future scientists? Quite the contrary: Scientists may use processes such as this to help focus on interesting phenomena more rapidly and to interpret their meaning.

Category:

Science & Technology

Tags:

• automatic reasoning
• artificial intelligence
• physics
• mathematics
• computer science
• experiments

[futureprogress]

amazing area of research... solid work. congrats...