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 ; Kapitel : 12 ; 387-409
10.09.2020
23 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Radial basis function , Response surface method , Design optimisation , Surrogate model , Kriging , Quality indicators Optimization , Astronomy, Astrophysics and Cosmology , Physics and Astronomy , Mathematical and Computational Engineering , Aerospace Technology and Astronautics , Physics , Computational Science and Engineering
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