Srivastava, AnkurMeade, Andrew J.2016-03-242016-03-242014Srivastava, Ankur and Meade, Andrew J.. "Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements." <i>International Journal of Aerospace Engineering,</i> 2014, (2014) Hindawi: http://dx.doi.org/10.1155/2014/218710.https://hdl.handle.net/1911/88641Wind tunnel tests to measure unsteady cavity flow pressure measurements can be expensive, lengthy, and tedious. In this work, the feasibility of an active machine learning technique to design wind tunnel runs using proxy data is tested. The proposed active learning scheme used scattered data approximation in conjunction with uncertainty sampling (US). We applied the proposed intelligent sampling strategy in characterizing cavity flow classes at subsonic and transonic speeds and demonstrated that the scheme has better classification accuracies, using fewer training points, than a passive Latin Hypercube Sampling (LHS) strategy.engThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Journal articlehttp://dx.doi.org/10.1155/2014/218710