Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design - Mingcheng Lei; Yufei Zhang (Theoretical and Applied Mechanics Letters, 2025)
Recent advances in contrastive language‒image pretraining (CLIP) models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation. Based on these developments, this study introduces a novel framework for airfoil design via natural language interfaces. To the authors’ knowledge, this study establishes the first end-to-end, bidirectional mapping between textual descriptions (e.g., “low-drag supercritical wing for transonic conditions”) and parametric airfoil geometries represented by class-shape transformation parameters. The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning, along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations. The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels. This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.