Learning Transferable Aerodynamic Laws across Physical Domains for Heterogeneous Flapping-Wing Systems

Flapping-wing flight involves strongly coupled interactions among rapid bilateral wing motion, body dynamics, and unsteady aerodynamic responses. Physical data that reveal how these processes interact are essential for data-driven aerodynamic modeling, system identification, state estimation, reduced-order dynamics, control-oriented analysis, and scientific machine learning. We introduce FlapKAD-State, a state-resolved synthetic dataset comprising 2,000 curated rigid-wing flight episodes and 720,152 valid time steps generated using synchronized rigid-body integration and quasi-steady aerodynamic modeling. Our dataset provides comprehensive bilateral flap and wing-rotation kinematics, together with their corresponding angular velocities, local body linear velocity, body angular velocity, and independently resolved aerodynamic forces for the left and right wings. These variables provide complementary descriptions of bilateral wing kinematics, body motion, and aerodynamic response within a unified episode structure. To facilitate the use and evaluation of the dataset, we provide standardized episode-level partitions, variable-length masks, channel definitions, physical units, coordinate conventions, and data-quality documentation, together with reference benchmarks that illustrate representative learning scenarios.