Energy, mass, speed. These three variables make up Einstein’s iconic equation E=MC2. But how did Einstein come to know of these concepts in the first place? A preliminary step to understanding physics is to identify the relevant variables. Without the concept of energy, mass and speed, even Einstein could not discover relativity. But can such variables be discovered automatically? This could dramatically speed up scientific discovery.
That’s the question Columbia Engineering researchers posed to a new AI program. The program was designed to observe physical phenomena through a video camera, then try to find the minimum set of fundamental variables that fully describe the observed dynamics. The study was published on July 25 in Computational science of nature.
The researchers began by feeding the system raw video footage of phenomena to which they already knew the answer. For example, they fed a video of a swinging double pendulum known to have exactly four “state variables” – the angle and angular velocity of each of the two arms. After a few hours of analysis, the AI produced the answer: 4.7.
“We thought that answer was pretty close,” said Hod Lipson, director of the Mechanical Engineering Department’s Creative Machines Lab, where the work was primarily done. “Especially since all the AI had access to raw video footage, without any knowledge of physics or geometry. But we wanted to know what the variables actually were, not just how many.”
The researchers then proceeded to visualize the actual variables identified by the program. Extracting the variables themselves was not easy, as the program cannot describe them in an intuitive way that would be understandable to humans. After some research, it turned out that two of the variables chosen by the program correspond loosely to the angles of the arms, but the other two remain a mystery.
“We tried to correlate the other variables with anything and everything we could think of: angular and linear velocities, kinetics and potential energyand various combinations of known quantities,” explained Boyuan Chen Ph.D., now an assistant professor at Duke University, who led the work. “But nothing seemed to fit perfectly. “The team was confident that the AI had found a valid set of four variables, because it made good predictions, “but we don’t yet understand the mathematical language it speaks,” he explained. .
After validating a number of other physical systems with known solutions, the researchers fed videos of systems for which they did not know the explicit answer. Early videos featured an “air dancer” waving in front of a local used car parking lot. After a few hours of analysis, the program returned eight variables. A video of a lava lamp also produced eight variables. They then fed a video clip of the flames to a holiday fireplace loop, and the program returned 24 variables.
A particularly interesting question was whether the set of variables was unique for each system or whether a different set was produced each time the program restarted.
“I always wondered if we encountered an intelligent extraterrestrial race, would they have discovered the same physical laws as us, or could they describe the universe in a different way?” said Lipson. “Perhaps some phenomena seem cryptically complex because we are trying to understand them using the wrong set of variables. In the experiments, the number of variables was the same each time the AI was restarted, but the specific variables were different. every time. So yes, there are other ways to describe the universe and it’s entirely possible our choices won’t be perfect.”
Researchers believe this type of AI can help scientists uncover complex phenomena for which theoretical understanding does not keep pace with the deluge of data – fields ranging from biology to cosmology. “Although we used video data in this work, any type of array data source could be used, radar arrays or DNA arrays, for example,” explained Kuang Huang, Ph.D. ., co-author of the article.
The work is part of Lipson and Fu Foundation mathematics professor Qiang Du’s decades-long interest in creating algorithms that can turn data into scientific laws. Older software systems, such as Lipson and Michael Schmidt’s Eureqa software, could distill free-form physical laws from experimental data, but only if the variables were identified in advance. But what if the variables are still unknown?
Lipson, who is also the James and Sally Scapa Professor of Innovation, argues that scientists can misinterpret or misunderstand many phenomena simply because they don’t have a good set of variables to describe the phenomena.
“For millennia, people knew about fast or slow moving objects, but it was not until the notion of speed and acceleration was formally quantified that Newton was able to discover his famous law of motion F = MA”, noted Lipson. The variables describing temperature and pressure had to be identified before the laws of thermodynamics could be formalized, and so on for all corners of the scientific world. Variables are a precursor to any theory.
“What other laws are we missing just because we don’t have the variablesasked Du, who co-directed the work.
The article was also co-authored by Sunand Raghupathi and Ishaan Chandratreya, who helped collect the data for the experiments.
Boyuan Chen et al, Automated Discovery of Hidden Fundamental Variables in Experimental Data, Computational science of nature (2022). DOI: 10.1038/s43588-022-00281-6
Quote: Roboticists Discover Alternative Physics (July 26, 2022) Retrieved July 27, 2022 from https://phys.org/news/2022-07-roboticists-alternative-physics.html
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