Geometric feature knowledge-driven surrogate-based optimization via aerodynamic supervised autoencoder

doi:10.1016/j.ast.2025.111028

Geometric feature knowledge-driven surrogate-based optimization via aerodynamic supervised autoencoder

2025-10-05

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DOI：10.1016/j.ast.2025.111028

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• An aerodynamic supervised autoencoder is proposed to learn geometric feature correlated with aerodynamic responses with limited aerodynamic data. • The correlation between geometric features and aerodynamic responses is utilized to guide the initial sampling toward regions near the optimum. • The Euclidean distance between the predicted solution and the current optimum in the feature space is used as a penalty term to enhance the effectiveness of infill sampling • The proposed optimization framework improves optimization efficiency by approximately twofold while achieving superior aerodynamic performance.

Geometric feature knowledge-driven surrogate-based optimization via aerodynamic supervised autoencoder

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