AI-Newton从零发现物理定律，无需先验知识
AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge
- 用可解释概念构建物理规律，逐步推广至更广领域
- 在含噪力学实验数据中成功复现牛顿第二定律等基本定律
- 适合对自主科学发现感兴趣的科研人员和教育者
当前基于AI的方法虽能从单个实验中推导出经验模型，却难以发现隐藏其后的共同基础物理规律——这正是人类物理学家擅长的领域。为弥合这一差距，我们提出AI-Newton，一种概念驱动的科学发现新框架。该系统能直接从原始多实验数据中自主推导通用物理定律，无需监督或先验物理知识。核心创新包括：(1) 提出可解释的物理概念以构造定律；(2) 逐步将定律泛化至更广泛领域。在大规模、含噪的力学实验数据集上，AI-Newton成功复现了牛顿第二定律、能量守恒与万有引力等基础且普遍的物理定律。这项工作标志着向自主、类人科学发现迈出了重要一步。
原文摘要 · Abstract (English)
While current AI-driven methods excel at deriving empirical models from individual experiments, a significant challenge remains in uncovering the common fundamental physics that underlie these models -- a task at which human physicists are adept. To bridge this gap, we introduce AI-Newton, a novel framework for concept-driven scientific discovery. Our system autonomously derives general physical laws directly from raw, multi-experiment data, operating without supervision or prior physical knowledge. Its core innovations are twofold: (1) proposing interpretable physical concepts to construct laws, and (2) progressively generalizing these laws to broader domains. Applied to a large, noisy dataset of mechanics experiments, AI-Newton successfully rediscovers foundational and universal laws, such as Newton's second law, the conservation of energy, and the universal gravitation. This work represents a significant advance toward autonomous, human-like scientific discovery.
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