Distilling free-form natural laws from experimental data. Export

@article{citeulike:4265776, abstract = {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.}, author = {Schmidt, Michael and Lipson, Hod}, citeulike-article-id = {4265776}, citeulike-linkout-0 = {http://dx.doi.org/10.1126/science.1165893}, citeulike-linkout-1 = {http://sciencemag.org/content/324/5923/81.abstract}, citeulike-linkout-2 = {http://sciencemag.org/content/324/5923/81.full.pdf}, citeulike-linkout-3 = {http://www.sciencemag.org/cgi/content/abstract/324/5923/81}, citeulike-linkout-4 = {http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2009Sci...324...81S}, citeulike-linkout-5 = {http://view.ncbi.nlm.nih.gov/pubmed/19342586}, citeulike-linkout-6 = {http://www.hubmed.org/display.cgi?uids=19342586}, day = {3}, doi = {10.1126/science.1165893}, issn = {1095-9203}, journal = {Science (New York, N.Y.)}, keywords = {ai, analysis, computer-science, genetic-algorithm, physics}, month = {April}, number = {5923}, pages = {81--85}, posted-at = {2009-04-03 15:57:53}, priority = {2}, title = {Distilling free-form natural laws from experimental data.}, url = {http://dx.doi.org/10.1126/science.1165893}, volume = {324}, year = {2009} }

TY - JOUR ID - citeulike:4265776 L3 - citeulike-article-id:4265776 N2 - 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. IS - 5923 JF - Science (New York, N.Y.) SN - 1095-9203 EP - 85 TI - Distilling free-form natural laws from experimental data. VL - 324 SP - 81 KW - ai KW - analysis KW - computer-science KW - genetic-algorithm KW - physics AU - Schmidt, Michael AU - Lipson, Hod PY - 2009/04/03/ UR - http://dx.doi.org/10.1126/science.1165893 ER -