Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
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SciPy 1.0 documents a mature open-source library that has become the de facto standard for scientific algorithms in Python with broad adoption across research projects.
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Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
SciPy 1.0 documents a mature open-source library that has become the de facto standard for scientific algorithms in Python with broad adoption across research projects.