CDI-Type I: Distilling Freeform Natural Laws from Experimental Data

CDI-Type I：从实验数据中提取自由形式的自然法则

• 批准号: 0941561
• 负责人: Hod Lipson
• 金额: $ 59.77万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941561&HistoricalAwards=false
• 关键词: CDI; Type; Distilling; Freeform; Natural

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to develop general-purpose algorithms for automatically identifying principles underlying observed phenomena, in fields ranging from physics to biology. For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power in scientific inquiry, finding natural laws and their corresponding equations has resisted automation. The approach is based on symbolic regression methods that can search for equations that describe invariant quantities in time-series data. Seeking invariants usually leads to deeper insights than prediction alone: For example, seeking invariant quantities in a pendulum motion leads to discovery of the conservation of energy.With respect to intellectual merit, the research seeks new algorithms to automatically search for implicit models that are conserved over the data and, simultaneously, search for new experiments that cause maximum disagreement between competing model predictions. A key challenge to finding analytic relationships automatically is defining algorithmically what makes a correlation in observed data significant and non-trivial. The ultimate milestone will be for the proposed method to identify yet-unknown laws in new systems such as biology, where natural laws and conservations are difficult to find.With respect to broader impact, the research addresses the need for methods to automatically and dynamically synthesize both the structure and parameters of models of nonlinear systems in many fields. The ability to actively infer models from behavior can be applied to create models of both engineered and natural systems from robotics to biological networks to human behavior.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。这项研究的目的是开发通用算法，用于自动识别从物理学到生物学等领域观察到的现象背后的原理。几个世纪以来，科学家们一直试图确定并记录自然界物理现象背后的分析规律。尽管计算能力在科学探究中普遍存在，但寻找自然规律及其相应的方程一直阻碍着自动化。该方法基于符号回归方法，可以搜索描述时间序列数据中不变量的方程。寻找不变量通常会带来比预测更深刻的见解：例如，在钟摆运动中寻找不变量会导致发现能量守恒。就智力价值而言，该研究寻求新的算法来自动搜索在数据上保守的隐式模型，同时搜索在相互竞争的模型预测之间引起最大分歧的新实验。自动发现分析关系的一个关键挑战是用算法定义什么使得观察数据中的相关性具有显著性和非琐碎性。最终的里程碑将是提出的方法在生物学等新系统中识别未知的规律，在这些系统中，自然规律和保护很难找到。就更广泛的影响而言，该研究解决了许多领域对非线性系统模型的结构和参数自动动态综合方法的需求。从行为中主动推断模型的能力可以应用于创建工程和自然系统的模型，从机器人到生物网络再到人类行为。