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Related Experiment Video

Updated: May 1, 2026

Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation

Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation

Published on: July 6, 2019

Evolving scientific discovery by unifying data and background knowledge with AI Hilbert.

Ryan Cory-Wright¹, Cristina Cornelio², Sanjeeb Dash³

¹Department of Analytics, Marketing and Operations, Imperial College Business School, London, UK. r.cory-wright@imperial.ac.uk.

Nature Communications

|July 14, 2024

Summary

This summary is machine-generated.

Scientists can now discover natural laws by integrating experimental data into the equation-discovery process. This new method uses polynomial optimization to derive scientific laws, improving efficiency and accuracy.

Related Experiment Videos

Last Updated: May 1, 2026

Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation

Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation

Published on: July 6, 2019

Area of Science:

Scientific discovery
Computational science
Mathematical physics

Background:

Discovering scientific laws traditionally involves manipulating equations based on existing knowledge, often excluding experimental data.
This indirect approach can be inefficient and may not fully leverage available information.

Purpose of the Study:

To develop a principled method for scientific discovery that directly incorporates experimental data.
To demonstrate the applicability of this method for deriving known scientific laws.

Main Methods:

Modeling scientific laws as polynomials and using binary variables and logical constraints.
Solving polynomial optimization problems via mixed-integer linear or semidefinite optimization.
Validating discovered scientific laws using Positivstellensatz certificates.

Main Results:

Successfully derived Kepler's Law of Planetary Motion from axioms and experimental data.
Derived the Radiated Gravitational Wave Power equation using the proposed method.
Demonstrated that the approach is widely applicable for polynomial scientific laws.

Conclusions:

The proposed method offers a more efficient and principled way to discover scientific laws by integrating experimental data.
This computational approach can accelerate scientific discovery and uncover new insights into natural phenomena.