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	सिमुलेशन-सहायता प्राप्त प्रतिक्रिया सतह कार्यप्रणाली	Robust Response Surface Methodology
क्षेत्र	प्रयोगात्मक अभिकल्प	प्रयोगात्मक अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1951 (RSM); simulation integration widely adopted from 1980s onward	1990
प्रवर्तक	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	G. G. Vining and Raymond H. Myers (dual response formulation)
प्रकार	Experimental optimization method	Experimental optimization technique
मौलिक स्रोत	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	Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology, 22(1), 38–45.
उपनाम	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM
संबंधित	6	3
सारांश	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.	Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor settings that hit a performance target while minimizing process variability, combining the empirical model-building power of classical RSM with the variance-reduction goals of robust parameter design.