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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The authors develop an input-schema identifiability certificate for physics-informed surrogates that decomposes lumen velocity in tubular flow into mesh-measurable tangent direction, boundary-condition-dependent magnitude, and signed-orientation ambiguity using a Cosserat-rod reduction.
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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.