Aerodynamic coefficient prediction of bio-inspired camber morphing wings with flexible surfaces using an explainable transformer

Bio-inspired morphing wings with flexible surfaces can enhance aerodynamic efficiency at low Reynolds numbers (Re), yet predicting their fluid-structure interaction remains challenging. We present PhysAero-MHANet, a physics-aware, interpretable deep learning framework coupled with controlled wind tunnel experiments for aerodynamic prediction of camber-morphing finite wings. The campaign yielded 911 samples spanning Re is an element of [3 x 10(4), 1 x 10(5)], camber deflections up to 10(degrees), and angles of attack from-18(degrees )to 18(degrees). Experiments showed up to 34% drag reduction at small angles of attack, a stall delay of approximate to 6(degrees), a maximum lift coefficient C-L,C-max approximate to 1.44, and a peak lift-to-drag ratio C-L/C-D approximate to 8.84. The proposed model is a transformer-based multi-task surrogate with physics-informed attention, hierarchical cross-feature fusion, and shapley additive explanations (SHAP) for interpretability. Against 11 machine-learning, deep-learning, and attention baselines, PhysAero-MHANet achieved R-2 approximate to 0.985 and MAPE < 12% across lift (C-L), drag (C-D), and rolling moment (C-M,C-R) predictions. These results provide new insight into morphing-wing aerodynamics and support real-time control, performance optimization, and integration into unmanned aerial vehicles (UAVs) and micro aerial vehicles (MAVs).