Although international travel is severely affected owing to the pandemic, learning how to better simulate turbulence in the air can help make better predictions and new research has just claimed to achieve that.

Researchers at the University of Illinois Urbana-Champaign have developed a new method that brings physics into the machine learning process to make better turbulence predictions.

"We don't know how to mathematically write down all of turbulence in a useful way. There are unknowns that cannot be represented on the computer, so we used a machine learning model to figure out the unknowns," said Jonathan Freund, Willett Professor and Head of the Department of Aerospace Engineering.

"We trained it on both what it sees and the physical governing equations at the same time as a part of the learning process. That's what makes it magic and it works".

People have been struggling to simulate turbulence and to model the unrepresented parts of it for a long time.

"We learned that if you try to do the machine learning without considering the known governing equations of the physics, it didn't work. We combined them and it worked," Freund added.

When designing an air or spacecraft, Freund said this method will help engineers predict whether or not a design involving turbulent flow will work for their goals.

They'll be able to make a change, run it again to get a prediction of heat transfer or lift, and predict if their design is better or worse.

"Anyone who wants to do simulations of physical phenomena might use this new method. It's a method that would admit other unknown physics. And the observed results of that unknown physics could be loaded in for training," Freund said in a paper published in the Journal of Computational Physics.