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@INPROCEEDINGS{Lagemann:280953,
      author       = {Lagemann, Christian and Paehler, Ludger and Callaham, Jared
                      and Mokbel, Sajeda and Ahnert, Samuel and Lagemann, Kai and
                      Lagemann, Esther and Adams, Nikolaus A. and Brunton, Steven
                      L.},
      title        = {{H}ydro{G}ym: {A} {R}einforcement {L}earning {P}latform for
                      {F}luid {D}ynamics},
      volume       = {283},
      publisher    = {ML Research Press},
      reportid     = {DZNE-2025-01035},
      pages        = {497 - 512},
      year         = {2025},
      comment      = {Proceedings of the 7th Annual Learning for Dynamics $\&$
                      Control Conference},
      booktitle     = {Proceedings of the 7th Annual Learning
                       for Dynamics $\&$ Control Conference},
      abstract     = {The modeling and control of fluid flows remain a
                      significant challenge with tremendous potential to advance
                      fields including transportation, energy, and medicine.
                      Effective fluid flow control can lead to drag reduction,
                      enhanced mixing, and noise reduction, among other
                      applications. While reinforcement learning (RL) has shown
                      great success in complex domains, such as robotics and
                      protein folding, its application to flow control is hindered
                      by the lack of standardized platforms and the computational
                      demands of fluid simulations. To address these challenges,
                      we introduce HydroGym, a solver-independent RL platform for
                      flow control research. HydroGym integrates sophisticated
                      flow control benchmarks, a scalable runtime, and
                      state-of-the-art RL algorithms. Our platform includes four
                      validated non-differentiable fluid flow environments and one
                      differentiable environment, all evaluated with a variety of
                      modern RL algorithms. HydroGym’s scalable design allows
                      computations to run seamlessly from laptops to
                      high-performance computing resources, providing a
                      standardized interface for implementing new flow
                      environments. HydroGym aims to bridge the gap in flow
                      control research, providing a robust platform to support
                      both non-differentiable and differentiable RL techniques,
                      fostering advancements in scientific machine learning.},
      month         = {Jun},
      date          = {2025-06-04},
      organization  = {7th Annual Learning for Dynamics and
                       Control Conference, Ann Arbor, MI
                       (USA), 4 Jun 2025 - 6 Jun 2025},
      cin          = {AG Mukherjee},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://pub.dzne.de/record/280953},
}