Physics-guided transfer learning. Credit: Nature (2026). DOI: 10.1038/s41586-026-10917-6
A platform for training and comparing machine learning models to actively reduce drag, improve lift, cut noise and manage heat has been launched by an international team that includes researchers at the University of Washington, University of Michigan Engineering, RWTH Aachen University and the Technical University of Munich.
"Fluid flows are central to several trillion-dollar industries, including energy, transportation, health and defense. An improved ability to understand and control these flows could have an immense economic and ecological impact, helping us to enable a better future," said Steven Brunton, senior co-corresponding author of the study in Nature and the Boeing Professor in AI & Data-Driven Engineering within UW mechanical engineering.
The large number of variables typically makes it impossible to directly calculate fluid behaviors in realistic scenarios. Now, the international team has built a platform focused on solving this problem through reinforcement learning, a form of machine learning that has already revolutionized fields like protein folding and nuclear fusion by training AI agents through interactions with their environments.
