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arXiv · 2504.01538

AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge

You-Le Fang·Dong-Shan Jian·Xiang Li·Yan-Qing Ma
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Abstract

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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You-Le Fang, Dong-Shan Jian, Xiang Li, Yan-Qing Ma. 2025-04-02. AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge. https://arxiv.org/abs/2504.01538

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