Pre-training AeroTransformer on nearly 30,000 diverse wing geometries and fine-tuning with 450 specific samples achieves 0.36% error on surface-flow prediction for transonic wings, an 84.2% reduction versus training from scratch.
They directly predict aerodynamic co- efficients from geometric parameters rather than from meshes
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Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design
Pre-training AeroTransformer on nearly 30,000 diverse wing geometries and fine-tuning with 450 specific samples achieves 0.36% error on surface-flow prediction for transonic wings, an 84.2% reduction versus training from scratch.