Response Surface Methods (RSMs) are statistical and numerical models that approximate the relationship between multiple input variables and an output variable. This chapter introduces the methodology and its importance for engineering design optimisation. The basic steps to build RSMs and validate the model accuracy are explained. An overview of three classical methods (Least Squares, Radial Basis Functions, and Kriging) is provided. A simple wing structure design optimisation problem is used to illustrate the different phases of the response surface methodology and its application to design optimisation. This example also includes the case of noisy data.
Response Surface Methodology
Optimization Under Uncertainty with Applications to Aerospace Engineering ; Chapter : 12 ; 387-409
2020-09-10
23 pages
Article/Chapter (Book)
Electronic Resource
English
Response surface method , Radial basis function , Kriging , Surrogate model , Quality indicators , Design optimisation Physics , Astronomy, Astrophysics and Cosmology , Aerospace Technology and Astronautics , Optimization , Mathematical and Computational Engineering , Computational Science and Engineering , Physics and Astronomy
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