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The HydroGym reinforcement learning platform for fluid dynamics
Nature
(2026) Cite this article
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.
Fluid flow control represents a critical challenge in several trillion-dollar industries including transportation (for example, aerospace, automotive, shipping), energy production (wind farms, gas turbines, pipelines), manufacturing (spray processes, additive manufacturing, thin films and coatings) and medicine (haemodynamic, respiratory and lymphatic systems). Advanced control systems could dramatically improve energy efficiency globally, for example reducing fuel consumption in the aviation industry by up to 15% through active drag reduction1,2,3,14,15, or increasing wind farm output by 4–5% through coordinated control16,17,18.
Despite decades of progress in traditional control approaches, the nonlinear, multiscale nature of turbulence results in control problems that are high-dimensional, non-convex and computationally intractable using conventional methods4,5,6,19. Recent breakthroughs in Reinforcement Learning (RL) have demonstrated transformative capabilities across increasingly complex scientific applications, with AlphaFold revolutionizing protein structure prediction7 and DeepMind achieving real-time control of fusion reactor plasmas8. These successes share a common enabler: benchmark environments that democratize research access and accelerate algorithmic innovation.
However, fluid dynamics has remained largely resistant to similar breakthroughs due to fundamental computational and accessibility barriers and a lack of comprehensive benchmark platforms for systematic algorithmic comparison and reproducible research. Training effective RL agents typically requires thousands or millions of interactions with the environment, and each evaluation in a fluid environment requires expensive computational fluid dynamics (CFD) simulations11,12,20. This bottleneck is magnified by the inherent sample inefficiency of model-free RL21,22, creating severe computational limi