Porovnat metody

	Metodologie povrchu odezvy asistovaná simulací	Centrální kompozitní design
Obor	Plánování experimentů	Plánování experimentů
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1951 (RSM); simulation integration widely adopted from 1980s onward	1951
Tvůrce	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	George E. P. Box and K. B. Wilson
Typ	Experimental optimization method	Response surface experimental design
Původní zdroj	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916025	Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.
Další názvy	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
Příbuzné	6	3
Shrnutí	Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings.	Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.