Accelerating High‐Fidelity Airfoil Design via Physics‐Informed Video Diffusion Model

ABSTRACT Airfoil design remains a challenging multi‐objective optimization problem requiring precise geometric control for optimal aerodynamic performance. Data‐driven inverse design methods mitigate this complexity but often yield non‐physical results when physical constraints are not explicitly incorporated. Here, we construct a high‐fidelity NACA–Nek1000 datasets with 53 400 samples from direct numerical simulations to train a dual physics‐informed video diffusion model (PVDM). The governing equations of incompressible flow are intrinsically coupled with the diffusion learning process, enabling the reconstruction of full‐field velocity and pressure distributions from lift‐to‐drag response sequences. The PVDM achieves accurate low‐Reynolds number airfoil reconstruction and real‐time flow generation within 30 s per case, approximately 600 times faster than conventional simulations, while maintaining over 90% fidelity. The reconstructed airfoils are further modeled as propeller geometries and experimentally validated, which well fit the simulated lift‐to‐drag response, establishing a rapid and generalizable framework for high‐fidelity, physics‐informed inverse aerodynamic design.