从实验数据中提取自由形式的自然规律
1Computational Biology, Cornell University, Ithaca, NY 14853, USA.
概括
科学家们开发了一种自动化方法,从数据中发现物理定律. 这种方法成功地在没有先前知识的情况下确定了物理学中的基本方程,加速了科学发现.
科学领域:
- 物理 物理学 物理
- 计算科学 计算科学
- 数据科学数据科学数据科学
背景情况:
- 识别控制物理现象的分析定律一直是长期以来的科学努力.
- 从观察到的数据中自动发现自然规律及其方程仍然是一个重大挑战,尽管计算能力的进步.
研究的目的:
- 开发一种自动化的方法来发现观察到的数据中的非碎的分析关系.
- 为了证明这种方法在从运动跟踪数据中发现基本物理定律方面的有效性.
主要方法:
- 提出了一个识别数据中非碎相关性的原则.
- 应用了一种算法来追踪来自不同物理系统 (例如,波器,双吊) 的运动数据.
- 该算法在没有对物理,动力学或几何学的先验知识的情况下运行.
主要成果:
- 成功发现了基本的物理定律,包括哈密尔顿定律,拉格兰定律以及几何和动量的保存定律.
- 通过使用从简单系统的规律来启动更复杂系统的解释来证明加速发现率.
- 发现了物理系统描述性定律的基本"字母表".
结论:
- 提出的原理和算法为自动发现物理定律提供了一种强大的方法.
- 这种方法有可能在各个领域显著加速科学理解.
- 该方法有效地识别了复杂系统中潜在的数学结构.
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