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Codes for density-matching, a new approach for optimization under uncertainty

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Density-Matching

Codes for replicating airfoil results in: <br > "A density-matching approach for optimization under uncertainty" <br > Pranay Seshadri, Paul Constantine, Gianluca Iaccarino, Geoffrey Parks

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Description of airfoil problem<br > Uncertainty: Inlet Mach number [0.66,0.69] with a beta(2,2)<br > Design Parameters: 16 Hicks-Henne bump function amplitudes (8 suction side, 8 pressure side)<br > Objective: Minimize distance to a target<br >

<br> Note: You will need numpy, scipy and SU2.

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Codes for density-matching, a new approach for optimization under uncertainty

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