A combined SHAP-guided MARL strategy using U-net predictions of skin-friction and wall pressure achieves 34.44% drag reduction and 34.01% net energy saving with 0.43% normalized input power in turbulent channel flow.
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Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.
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Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
A combined SHAP-guided MARL strategy using U-net predictions of skin-friction and wall pressure achieves 34.44% drag reduction and 34.01% net energy saving with 0.43% normalized input power in turbulent channel flow.
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Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.