The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Organizations: Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States · AI Institute in Dynamic Systems, University of Washington, Seattle, WA 98195, United States · Chair of Fluid Mechanics and Institute of Aerodynamics, RWTH Aachen University, Aachen, Germany · Data-Driven Fluid Engineering Laboratory, Inha University, Incheon, South Korea · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, United States · FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden · School of Engineering and Design, Technical University of Munich, Munich, Germany · Arts et Métiers Institute of Technology, CNAM, DynFluid, HESAM Université, Paris, France · Munich Institute of Integrated Materials, Energy and Process Engineering, Technical University of Munich, Munich, Germany · JARA Center for Simulation and Data Science, RWTH Aachen University, Aachen, Germany · MediaTek Research, London, United Kingdom · Statistics and Machine Learning, German Center for Neurodegenerative Diseases, Bonn, Germany
Abstract
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially unlocking new technologies. Yet controlling fluids is hard: the dynamics are high-dimensional, nonlinear, and multiscale. While reinforcement learning (RL) has recently succeeded in robotics and protein folding through shared benchmarks, fluid dynamics has resisted such progress: each controller is typically tuned to a single geometry and operating point, making results hard to accumulate, transfer, and compare. We introduce HydroGym, a solver-independent RL platform for flow control, and show that standardized infrastructure unlocks transferable control intelligence across flow regimes. HydroGym provides 61+ validated environments spanning laminar to turbulent flows, with systematic Reynolds number progressions up to Re=400,000 and Mach number variations in 2D and 3D. It supports diverse backends, including finite-volume, spectral-element, finite-element, lattice-Boltzmann, and fully differentiable solvers for gradient-enhanced optimization. Across environments, RL agents consistently discover robust control principles, such as boundary-layer manipulation, acoustic-feedback disruption, and wake reorganization, yielding drag reductions exceeding 90% in canonical configurations. Critically, we demonstrate zero-shot transfer: agents trained only on a simplified channel flow achieve 38% friction-drag reduction on an unseen 3D wing section at chord Reynolds number Re=200,000 reducing exploration costs by four orders of magnitude versus direct on-wing optimization. This suggests RL agents uncover essential physics rather than configuration-specific patterns, pointing toward generalizable control. HydroGym offers extensible, scalable community infrastructure for fluid dynamics, machine learning, and control research.