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.
Multiscale analysis of the topological invariants in the logarithmic region of turbulent channels at a friction Reynolds number of 932
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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.