Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Hybride Experimenteel Ontwerp× | Ontwerp van Experimenten (DoE) ondersteund door simulatie× | |
|---|---|---|
| Vakgebied | Experimenteel ontwerp | Experimenteel ontwerp |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1989–2000s | 1970s–1990s (formalized with computer experimentation growth) |
| Grondlegger≠ | 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 |
| Oorspronkelijke bron | 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 |
| Aliassen | hybrid DOE, combined experimental design, mixed experimental design, hybrid experimental strategy | Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE |
| Verwant≠ | 4 | 5 |
| Samenvatting≠ | 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. |
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