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Updated: Jun 24, 2026
11:03
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
Distilling free-form natural laws from experimental data.
1Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Science (New York, N.Y.)
|April 4, 2009
Summary
Scientists developed an automated method to discover physical laws from data. This approach successfully identified fundamental equations in physics without prior knowledge, accelerating scientific discovery.
Area of Science:
- Physics
- Computational Science
- Data Science
Background:
- Identifying analytical laws governing physical phenomena has been a long-standing scientific endeavor.
- Automating the discovery of natural laws and their equations from observed data remains a significant challenge despite advances in computing power.
Purpose of the Study:
- To develop an automated approach for discovering nontrivial analytical relations within observed data.
- To demonstrate the effectiveness of this approach in uncovering fundamental physical laws from motion-tracking data.
Main Methods:
- Proposed a principle for identifying nontrivial correlations in data.
- Applied an algorithm to motion-tracking data from diverse physical systems (e.g., harmonic oscillators, double-pendula).
- The algorithm operated without prior knowledge of physics, kinematics, or geometry.
Main Results:
- Successfully discovered fundamental physical laws, including Hamiltonians, Lagrangians, and conservation laws for geometric and momentum.
- Demonstrated accelerated discovery rates by using laws from simpler systems to bootstrap explanations for more complex systems.
- Uncovered a foundational 'alphabet' of descriptive laws for physical systems.
Conclusions:
- The proposed principle and algorithm offer a powerful method for automating the discovery of physical laws.
- This approach has the potential to significantly accelerate scientific understanding across various domains.
- The method effectively identifies underlying mathematical structures in complex systems.
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