Physicists have used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system.
The journal Proceedings of the National Academy of Sciences published the findings by experimental and theoretical physicists at Emory University, based on a neural network model and data from laboratory experiments on dusty plasma—ionized gas containing suspended dust particles.
The work is one of the relatively few instances of using AI not as a data processing or predictive tool, but to discover new physical laws governing the natural world.
“We showed that we can use AI to discover new physics,” says Justin Burton, an Emory professor of experimental physics and senior co-author of the paper. “Our AI method is not a black box: we understand how and why it works. The framework it provides is also universal. It could potentially be applied to other many-body systems to open new routes to discovery.”
The PNAS paper provides the most detailed description yet for the physics of a dusty plasma, yielding precise approximations for non-reciprocal forces.
“We can describe these forces with an accuracy of more than 99%,” says Ilya Nemenman, an Emory professor of theoretical physics and co-senior author of the paper.
“What’s even more interesting is that we show that some common theoretical assumptions about these forces are not quite accurate. We’re able to correct these inaccuracies because we can now see what’s occurring in such exquisite detail.”
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