Sammenlign metoder

	Hybrid eksperimentdesign	Simuleringsassistert forsøksdesign
Fagfelt	Forsøksdesign	Forsøksdesign
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1989–2000s	1970s–1990s (formalized with computer experimentation growth)
Opphavsperson	Multiple contributors; notably Sacks, Welch, Mitchell & Wynn (computer experiments); broader hybrid concept developed across 1980s–2000s	Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.
Type	Combined experimental design strategy	Hybrid experimental-computational method
Opprinnelig kilde	Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-1441929921	Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202
Alias	hybrid DOE, combined experimental design, mixed experimental design, hybrid experimental strategy	Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE
Relaterte	4	5
Sammendrag	Hybrid design of experiments (hybrid DOE) combines two or more experimental design strategies within a single study to exploit the complementary strengths of each. Common combinations include factorial or fractional-factorial arrays paired with computer simulation runs, space-filling Latin hypercube designs merged with response surface augmentations, or Taguchi orthogonal arrays integrated with response surface methodology. The approach is widely used when a single design type cannot efficiently cover all phases of an engineering investigation — from screening through to optimization.	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.