Radial Basis Functions and Kriging Metamodels for Aerodynamic Optimization

By Praveen Chandrashekarappa, R. Duvigneau. 2007

51 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/728cb7ce782bcfafaba2c1cd1f6bfc912cfd79de

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Citations in the corpus

• Kriging is well-suited to parallelize optimization (2010, unknown): “Classical surrogates include radial basis functions [37], interpolation splines [52], neural nets [8] ( deterministic metamodels), or linear and non-linear regression [2], and Kriging [7] ( probabilistic metamodels).”
• Improving variable-fidelity modelling by exploring global design space and radial basis function networks for aerofoil design (2015, methodology): “RBF interpolation is widely used in many different fields, such as optimization (Praveen and Duvigneau, 2007; Xing et al., 2014), medical imaging (Carr, Fright, and Beatson, 1997), system control (Boyd, 2013; Heuer and Tran, 2013) and others.”