Salīdzināt metodes

	Metodoloģija ar simulācijas palīdzību atbildes virsmas modelēšanai	Metodoloģija virsmas atbildes (RSM)
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Hypothesis test
Izcelsmes gads	1951 (RSM); simulation integration widely adopted from 1980s onward	1951
Autors	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	George E. P. Box & K. B. Wilson
Tips	Experimental optimization method	Second-order polynomial response surface model
Pirmavots	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.
Citi nosaukumi	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	RSM, Central Composite Design, Box-Behnken Design, CCD
Saistītās	6	7
Kopsavilkums	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.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.