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	Projektowanie eksperymentów wspomagane symulacją	Metodologia Powierzchni Odpowiedzi (RSM)
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Hypothesis test
Rok powstania	1970s–1990s (formalized with computer experimentation growth)	1951
Twórca	Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.	George E. P. Box & K. B. Wilson
Typ	Hybrid experimental-computational method	Second-order polynomial response surface model
Źródło pierwotne	Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202	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.
Inne nazwy	Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE	RSM, Central Composite Design, Box-Behnken Design, CCD
Pokrewne	5	7
Podsumowanie	Simulation-assisted design of experiments (SA-DoE) integrates computational simulation tools — such as finite element analysis (FEA), computational fluid dynamics (CFD), or discrete-event simulation — with classical DoE principles to systematically explore the factor space of a system. Rather than running costly or hazardous physical trials, researchers execute a structured set of virtual experiments across selected factor combinations, then fit a surrogate model to the simulation outputs to understand main effects, interactions, and optimal 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.