Linganisha mbinu

	Ubunifu wa Majaribio Usaidiziwa na Uhuishaji	Muundo wa Majaribio
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1970s–1990s (formalized with computer experimentation growth)	1935
Mwanzilishi	Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.	Ronald A. Fisher
Aina	Hybrid experimental-computational method	Experimental planning framework
Chanzo asilia	Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Majina mbadala	Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE	DOE, experimental design, factorial experimentation, planned experimentation
Zinazohusiana	5	3
Muhtasari	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.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.