物理定律
守恒定律
动量(技术分析)
混乱的
运动学
钥匙(锁)
牛顿运动定律
幂律
过程(计算)
简谐运动
计算机科学
简单(哲学)
鉴定(生物学)
牙石(牙科)
法学
经典力学
数学
人工智能
物理
数学分析
计算机安全
认识论
操作系统
统计
医学
哲学
经济
植物
牙科
生物
政治学
量子力学
财务
作者
Michael Schmidt,Hod Lipson
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2009-04-03
卷期号:324 (5923): 81-85
被引量:2272
标识
DOI:10.1126/science.1165893
摘要
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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