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	Progettazione di Esperimenti Assistita da Simulazione	Analisi di Sensibilità - Progettazione Integrata degli Esperimenti
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1970s–1990s (formalized with computer experimentation growth)	1990s–2000s (formal integration emerged in simulation and engineering optimization literature)
Ideatore	Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.	Integrated approach drawing on Saltelli et al. (sensitivity analysis) and Montgomery (DoE); no single originator
Tipo	Hybrid experimental-computational method	Hybrid experimental-analytical framework
Fonte seminale	Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202	Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley. ISBN: 9780470870938
Alias	Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE	SA-DoE, SA-integrated DoE, DoE with sensitivity screening, factor screening with sensitivity analysis
Correlati	5	3
Sintesi	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.	Sensitivity Analysis-Integrated Design of Experiments (SA-DoE) combines systematic experimental planning with formal sensitivity analysis to identify which input factors most strongly influence a response, then efficiently characterises those factors' effects. By embedding sensitivity screening into the DoE workflow, experimenters avoid wasting trials on inert variables and focus resources on the factors that truly drive system behaviour — making it especially valuable in simulation studies, product engineering, and complex process optimisation.