Distilling Free-Form Natural Laws from Experimental Data

American Association for the Advancement of Science (AAAS) - Tập 324 Số 5923 - Trang 81-85 - 2009
Michael Schmidt1,2,3, Hod Lipson2,3
1Computational Biology, Cornell University, Ithaca, NY 14853, USA.
2Computing and Information Science, Cornell University, Ithaca, NY, 14853, USA
3School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA

Tóm tắt

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the “alphabet” used to describe those systems.

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This research was supported in part by Integrative Graduate Education and Research Traineeship program in nonlinear systems a U.S. NSF graduate research fellowship and NSF Creative-IT grant 0757478 and CAREER grant 0547376. We thank M. Kurman for editorial consultation and substantive editing of the manuscript.

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American Association for the Advancement of Science (AAAS)

Tập 324 Số 5923

81-85

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