Professor Mario Rüttgers of the Department of Mechanical Engineering at SUNY Korea has contributed as a co-author to a new study published in Nature. The work introduces HydroGym, a computational platform that brings together reinforcement learning and computational fluid dynamics to develop intelligent strategies for controlling fluid flows.
One of the study’s key findings is that reinforcement-learning agents can learn flow-control strategies in one computational environment and transfer them to substantially different environments without retraining. The authors demonstrate this capability across different flow configurations, including the transfer from a simple channel flow to a three-dimensional aircraft-wing model.
The study points toward a broader challenge for the use of artificial intelligence in the physical sciences: the computational environment itself becomes part of what the AI system experiences and learns. Choices such as geometry, boundary conditions, flow regimes, numerical resolution, observations, and control actions can therefore influence the resulting behavior.
By providing shared computational environments for reinforcement-learning research in fluid dynamics, HydroGym offers new opportunities to investigate how AI can learn transferable physical principles and contribute to more general and autonomous approaches to flow control in real-world applications.
Professor Rüttgers joined SUNY Korea as an Assistant Professor in the Department of Mechanical Engineering in Fall 2026. He received his Ph.D. in Mechanical Engineering from RWTH Aachen University and his M.S. in Mechanical Engineering from Pohang University of Science and Technology (POSTECH).
Article link: https://www.nature.com/articles/s41586-026-10917-6
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