USC is among the first universities chosen to help lead the U.S. Department of Energy’s Genesis Mission, a national initiative uniting government laboratories, universities and private industry to explore artificial intelligence for scientific discovery, officials said Wednesday.
USC will lead a multi-institutional research team developing a new kind of AI designed to tackle turbulence, a longstanding challenge in physics and engineering that affects technologies people rely on every day. Officials said more accurate predictions could help engineers build more efficient aircraft, strengthen the nation’s energy infrastructure and speed the development of technologies critical to U.S. competitiveness.
“At USC, we’re building an ecosystem where human-centered AI, scientific discovery and cross-sector collaboration reinforce one another,” said Gaurav Sukhatme, interim dean of the USC Viterbi School of Engineering and inaugural director of the newly named USC Mark and Mary Stevens School of Computing and Artificial Intelligence.
“The Genesis Mission reflects that vision by bringing together universities, national laboratories and industry to advance discovery while preparing the next generation of scientists and engineers,” he added.
USC is collaborating with the University of Michigan and Argonne National Laboratory to develop AI that can accelerate one of the most computationally demanding tasks in science: predicting turbulent flows.
“Consider the airflow around a commercial aircraft,” said Iván Bermejo-Moreno, associate professor of aerospace and mechanical engineering at USC Viterbi and the project’s principal investigator. “Predicting how turbulence evolves means following millions of tiny motions in the air as they interact — more than even today’s fastest supercomputers can calculate.”
Instead, scientists use mathematical models to estimate the smallest turbulent motions. Bermejo-Moreno’s team is developing AI trained on the laws of physics and advanced computer simulations to recognize recurring patterns in turbulence and predict how they will evolve.
“Researchers have applied AI to turbulence before, but our approach is different,” Bermejo-Moreno said. “We’re teaching AI to recognize the flow structures themselves — the same kinds of swirling patterns you see in waterfalls or certain clouds — and use them to improve our predictions.”
The approach could make scientific simulations faster and more accurate, allowing researchers to solve problems that would otherwise take years to compute or remain beyond Wednesday’s computing capabilities.
Although the team’s first application is turbulence, the underlying AI could eventually improve simulations across disciplines.