Offline-trained recurrent neural estimator for opposition control in turbulence fails in closed loop due to controller-induced distribution shift but is stabilized by closed-loop retraining and spectral consistency on actuation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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physics.flu-dyn 2years
2026 2representative citing papers
Reward hacking in RL for wall-turbulence control is exposed through mass-conserving projection, memoryless policies, and pressure-gradient rewards; fixes yield honest 17% drag reduction.
citing papers explorer
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Offline accuracy is not enough: closed-loop instability and stabilisation of a wall-sensor neural estimator in opposition control
Offline-trained recurrent neural estimator for opposition control in turbulence fails in closed loop due to controller-induced distribution shift but is stabilized by closed-loop retraining and spectral consistency on actuation.
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Reward hacking in physical reinforcement learning revealed by turbulent drag reduction
Reward hacking in RL for wall-turbulence control is exposed through mass-conserving projection, memoryless policies, and pressure-gradient rewards; fixes yield honest 17% drag reduction.