An artificial intelligence tool has examined physical systems and not surprisingly, found new ways of describing what it found.
How do we make sense of the universe? There’s no manual. There’s no prescription.
At its most basic, physics helps us understand the relationships between “observable” variables – these are things we can measure. Velocity, energy, mass, position, angles, temperature, charge. Some variables like acceleration can be reduced to more fundamental variables. These are all variables in physics which shape our understanding of the world.
These variables are bound together through equations.
Albert Einstein’s most famous equation, E = mc2, summarises the relationship between the variables energy (E) and mass (m), using the constant: the speed of light (c). In fact, all of Einstein’s very complicated Theory of Special Relativity can be reduced to relationships between three variables: energy, mass and velocity.
There’s nothing sacred about our choice of variable. The variables and mathematics we choose have stood the test of time as the ones that make sense for a given theory or physical system.
But, what if we were to find other physical variables to solve the same problems? It wouldn’t change the problem… or the solution. But it might give us new insights into the inner workings of the universe and accelerate scientific discovery.
Read more: Machine learning identifies the origin of the most famous Martian meteorite to land on Earth
Now, an artificial intelligence (AI) tool developed at Columbia University in New York has done just that. The results of the experiments are published in Nature Computational Science.
Roboticists at Columbia Engineering developed an AI program to review raw video data and search for the minimal set of fundamental variables that fully describe the observed physical dynamics of the system the swinging of a pendulum.
To test their AI, the team first showed the tool videos of a phenomenon for which they already knew the answer.
Double pendula can be described with exactly four “state variables” – the angle and angular velocity of each of the two arms. After staring at the videos for a few hours, the AI gave its answer for the number of variables in the system: 4.7.
“We thought this answer was close enough,” says senior author Hod Lipson, director of the Creative Machines Lab in Columbia University’s Department of Mechanical Engineering. “Especially since all the AI had access to was raw video footage, without any knowledge of physics or geometry. But we wanted to know what the variables actually were, not just their number.”
So, the next challenge was to try and visualise the variables that the AI had identified. This was not easy as the program does not describe the variables in language intuitive to humans. The researchers did, however, correlate two of the variables to the angles of each pendulum’s arm.
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“We tried correlating the other variables with anything and everything we could think of: angular and linear velocities, kinetic and potential energy, and various combinations of known quantities,” explains lead author Boyuan Chen, now an assistant professor at Duke University. “But nothing seemed to match perfectly.”
Boyuan Chen explains the team’s research
Boyuan Chen, now an assistant professor at Duke University says the team …….
Source: https://cosmosmagazine.com/technology/artificial-intelligence-physics-variables/