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
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
Selected Papers on Response Surface Methodology
NTIS | 1972
|An Introduction to Response Surface Methodology
NTIS | 1964
|Assessment of Pushover Response Parameters Using Response Surface Methodology
Springer Verlag | 2020
|A Hybridized Self-Organizing Response Surface Methodology
British Library Conference Proceedings | 2008
|