chaos

Chaos is all around us, a fact that weather forecasters know all too well.Their job is notoriously difficult because small changes in air pressure or temperature, which ultimately drive winds and weather systems, can have huge consequences on a global scale. This sensitivity to tiny differences is commonly called the butterfly effect, and it makes weather patterns chaotic and hard to predict.Chaos pops up in many other places, too, and scientists have studied its role in physics for more than a century. But only since the 1980s have physicists investigated the connections between chaos and quantum mechanics—the most fundamental theory we have about the building blocks of the universe.One wrinkle in studying quantum chaos is that quantum physics itself seems to forbid chaotic behavior. The rules that govern the quantum world are actually too simple to give rise to the same kind of unpredictability as the weather. This prompted researchers to examine the differences between ordinary chaotic systems and their quantum counterparts more closely, a task that has been stalled because scientists lack the mathematical tools to quantify chaos in a quantum setting.Now, researchers from the Joint Quantum Institute (JQI) and the Condensed Matter Theory Center (CMTC) at the University of Maryland have used a promising diagnostic tool to characterize one of the simplest systems that physicists use to study chaos. This new diagnostic tracks the emergence of quantum interference effects and shows that they eventually destroy ordinary chaotic behavior. The work, performed by JQI and CMTC graduate student Efim Rozenbaum and two collaborators, was published online in Physical Review Letters on Feb. 21.

In many situations, chaos makes it nearly impossible to predict what will happen next. Nowhere is this more apparent than in weather forecasts, which are notorious for their unreliability. But the clever application of artificial intelligence can help reign in some chaotic systems, making them more predictable than ever before.

In this episode of Relatively Certain, Dina sits down with Michelle Girvan, a physics professor at the University of Maryland (UMD), to talk about how artificial intelligence can help predict chaotic behavior, as well as how combining machine learning with conventional physics models might yield even better predictions and insights into both methods.