Efficient Bilevel Surrogate Approach for Optimization Under Uncertainty of Shock Control Bumps

What is it about?

When adding uncertainties to optimization problems, the computational cost is significantly increased. We develop an efficient framework for optimization under uncertainty and apply it to the robust design of a shock control bump over an airfoil. The framework uses surrogate models to reduce the number of samples required to find a global robust configuration.

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