Journal Article

DZNE-2026-00955

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png The HydroGym reinforcement learning platform for fluid dynamics.

Lagemann, C.Extern* ; Mokbel, S. ; Gondrum, M. ; Rüttgers, M. ; Wang, Y. ; Suárez, P. ; Paehler, L. ; Bezgin, D. A. ; Buhendwa, A. B. ; Callaham, J. L. ; Ahnert, S. ; Zolman, N. ; Shao, X. ; Loiseau, J.-C. ; Adams, N. A. ; Meinke, M. ; Schröder, W. ; Lagemann, K.DZNE* ; Lagemann, E. ; Vinuesa, R. ; Brunton, S. L.

2026
Nature Publ. Group London [u.a.]

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Please use a persistent id in citations: doi: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 noise1-3. Yet fluids are notoriously difficult to control because they involve high-dimensional, nonlinear and multiscale dynamics that resist conventional approaches4-6. Reinforcement learning has driven remarkable progress in fields such as protein folding and complex games, which have shared benchmarks and standardized environments7-10. 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 compare11-13. 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.

Keyword(s): Reinforcement Machine Learning (MeSH) ; Hydrodynamics (MeSH)

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Contributing Institute(s):

1. Statistics and Machine Learning (AG Mukherjee)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

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; BIOSIS Previews ; Biological Abstracts ; Chemical Reactions ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; Current Contents - Physical, Chemical and Earth Sciences ; DEAL Nature ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 60 ; Index Chemicus ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record

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