Wind tunnel tests to measure unsteady cavity flow pressure measurements can be expensive, lengthy, and tedious. In this work, the\nfeasibility of an active machine learning technique to design wind tunnel runs using proxy data is tested. The proposed active\nlearning scheme used scattered data approximation in conjunction with uncertainty sampling (US). We applied the proposed\nintelligent sampling strategy in characterizing cavity flowclasses at subsonic and transonic speeds and demonstrated that the scheme\nhas better classification accuracies, using fewer training points, than a passive Latin Hypercube Sampling (LHS) strategy.
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