2026-08-26 ミシガン大学
A HydroGym benchmark environment simulating 3D stalled flow over an airfoil (shape of an airplane wing) at a high angle of attack (as if the plane’s nose were pointed too high). The visualization shows vortex structures (colored by rotational direction: blue for clockwise, red for counter-clockwise) observed by reinforcement learning agents that learn how to suppress turbulence and prevent aerodynamic stall. Image credit: Christian Lagemann, University of Washington
＜関連情報＞
- https://news.umich.edu/hydrogym-trains-assesses-ai-for-actively-controlling-fluid-dynamics/
- https://www.nature.com/articles/s41586-026-10917-6
流体力学のための強化学習プラットフォーム「HydroGym」 The HydroGym reinforcement learning platform for fluid dynamics
Christian Lagemann,Sajeda Mokbel,Miro Gondrum,Mario Rüttgers,Yuning Wang,Pol Suárez,Ludger Paehler,Deniz A. Bezgin,Aaron B. Buhendwa,Jared L. Callaham,Samuel Ahnert,Nicholas Zolman,Xiao Shao,Jean-Christophe Loiseau,Nikolaus A. Adams,Matthias Meinke,Wolfgang Schröder,Kai Lagemann,Esther Lagemann,Ricardo Vinuesa &Steven L. Brunton
Nature Published:19 August 2026
DOI:https://doi.org/10.1038/s41586-026-10917-6
Abstract
Effective control of fluid flows is critical across transportation, energy and medicine, where it can increase lift, reduce drag, enhance mixing and attenuate noise1,2,3. Yet fluids are notoriously difficult to control because they involve high-dimensional, nonlinear and multiscale dynamics that resist conventional approaches4,5,6. Reinforcement learning has driven remarkable progress in fields such as protein folding and complex games, which have shared benchmarks and standardized environments7,8,9,10. Fluid dynamics has lacked such infrastructure, so each controller is typically tuned to a single geometry and operating condition, making progress difficult to accumulate, transfer and compare11,12,13. Here we introduce HydroGym, a solver-independent reinforcement learning platform providing more than 60 validated, openly available flow control environments spanning from canonical laminar flows to complex turbulent flows, with systematic progression in the Reynolds number up to Re = 4 × 105, and Mach number variations in two and three dimensions. Across these environments, agents repeatedly discover robust control principles, including boundary layer manipulation, disruption of acoustic feedback and reorganization of turbulent wakes. Critically, we demonstrate a proof of concept for zero-shot transfer, in which agents that are trained exclusively in inexpensive surrogate environments are deployed to challenging real-world scenarios such as a three-dimensional wing section. We achieve a 38% reduction in local skin friction while reducing exploration costs by four orders of magnitude compared with direct on-wing optimization. As this transfer exploits shared near-wall physics, the breadth of generalization remains open, suggesting a new pathway for research toward policy generalization across computationally prohibitive simulation environments. By offering a common, extensible foundation for reproducible research, HydroGym moves flow control from isolated case studies toward a cohesive community effort.
