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We present a sample-efficient way to train data-driven models of off-design aerodynamic constraints. The training data volume is reduced by 95% using the proposed method, and the model generalizability is verified in multiple airfoil optimization problems.
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This page is a summary of: Efficient Data-Driven Off-Design Constraint Modeling for Practical Aerodynamic Shape Optimization, AIAA Journal, July 2023, American Institute of Aeronautics and Astronautics (AIAA),
DOI: 10.2514/1.j062629.
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