arXiv AI
Jul 1
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
arXiv:2512. 17534v2 Announce Type: replace-cross Abstract: Modeling and controlling fluids is critical across science and engineering.
Glass-Box Deep Reinforcement Learning for High-Speed Flow Control
The article discusses how glass-box deep reinforcement learning (DRL) can discover actuation strategies for high‑speed, nonlinear, unsteady flows without explicit models, potentially revealing mechanisms beyond classical control. It highlights the challenges of applying DRL to real high‑speed flows, including simulation cost, data efficiency, sensing in extreme environments, reward design, explainability, and generalization across Mach and Reynolds numbers. The authors argue that overcoming these constraints will require physics‑aware learning, robust real‑time operation, and extensive validation to translate DRL capabilities into reliable, certified technology.
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