Glass-Box Deep Reinforcement Learning for High-Speed Flow Control

Key points

• By learning directly from interaction with a flow, DRL agents can find actuation strategies for nonlinear, unsteady flows without explicit models, often exploiting mechanisms that classical control would not anticipate.1
• High-speed flows are a demanding test of this promise: shocks, shock/boundary-layer interactions, transition, aerothermal loading and microsecond-scale dynamics strain both algorithms and hardware.1
• In this perspective, we examine what constrains DRL-based high-speed flow control, including compressible simulation cost and fidelity, data efficiency, sensing in extreme environments, reward design, explainability, and generalization across Mach and Reynolds number.1
• We argue that these limits reflect a tension between data-driven optimization and the structured physics, constraints and uncertainties of real flows.1

Sources (1)

• [1]Glass-Box Deep Reinforcement Learning for High-Speed Flow Control
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:30 PM

  Deep reinforcement learning (DRL) is challenging long-held assumptions in active flow control.

  By learning directly from interaction with a flow, DRL agents can find actuation strategies for nonlinear, unsteady flows without explicit models, often exploiting mechanisms that classical control would not anticipate.

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