Many sciences that deal with big sets of data use machine learning, even in the case of theoretical data. James Halverson, an assistant professor of physics at Northeastern University, is using data science to study string theory, which is predicted as one of the fundamental laws of physics that govern the universe. String theory predicts that the universe is made up of tiny, thread-like loops of concentrated energy called strings. Halverson is looking into machine learning to help overcome computational hurdles in string theory.
“String theory is not a settled subject,” he says. “This is a complex problem, so we need not just modern techniques from mathematics, but also modern techniques from computer science.”
The theory predicts that there are extra dimensions beyond the four dimensions that we experience every day: time and three dimensions of space. These theoretical extra dimensions are hard to visualize, and there are many possible ways that these various geometries could be folded in on themselves and hidden in our universe.
Applications of machine learning in other scientific fields like laboratory experiments run by biologists have also inspired Halverson. The way that proteins fold is actually a pretty good analogy to some of the problems that we run into in string theory, he says. Though no complex system out there is going to be a perfect analogy, we might be able to draw some inspiration from what people are doing in other fields.
Halverson interacts with leaders in the tech industry to help them engage with physics research and explore potential scientific applications of the techniques they have developed. In April, he helped organise a meeting between researchers from machine learning and physics at Microsoft’s headquarters outside Seattle.
Using data science to learn more about the large set of possibilities in string theory could ultimately help scientists in a better understanding of how theoretical physics fits into findings from experimental physics. Halverson says one of the ongoing questions in the field is how to unify string theory with experimental findings from particle physics and cosmology, which he describes as the physics of the smallest of the small and the biggest of the big.