formación

formación

formación[{” attribute=””>Columbia University AI program observed physical phenomena and uncovered relevant variables—a necessary precursor to any physics theory. But the variables it discovered were unexpected.

Energy, Mass, Velocity. These three variables make up Einstein’s iconic equation E=MC2. But how did Albert Einstein know about these concepts in the first place? Before understanding physics you need to identify relevant variables. Not even Einstein could discover relativity without the concepts of energy, mass, and velocity. But can variables like these be discovered automatically? Doing so would greatly accelerate scientific discovery.

This is the question that Columbia Engineering researchers posed to a new artificial intelligence program. The AI program was designed to observe physical phenomena through a video camera and then try to search for the minimal set of fundamental variables that fully describe the observed dynamics. The study was published in the journal Nature Computational Science on July 25.

formación
formación

formación

formación

formación

formación

formación
formación

formación

formación

formación

formación[{” attribute=””>DNA arrays, for example,” explained Kuang Huang PhD ’22, who coauthored the paper.

The work is part of Lipson and Fu Foundation Professor of Mathematics Qiang Du’s decades-long interest in creating algorithms that can distill data into scientific laws. Past software systems, such as Lipson and Michael Schmidt’s Eureqa software, could distill freeform physical laws from experimental data, but only if the variables were identified in advance. But what if the variables are yet unknown?

formación
formación

formación

formación[{” attribute=””>University of Washington

, Columbia, and Harvard NSF AI institute for dynamical systems, aimed to accelerate scientific discovery using AI.

Reference: “Automated discovery of fundamental variables hidden in experimental data” by Boyuan Chen, Kuang Huang, Sunand Raghupathi, Ishaan Chandratreya, Qiang Du and Hod Lipson, 25 July 2022, Nature Computational Science.
DOI: 10.1038/s43588-022-00281-6

formación

Leave a Comment