ENBIS-22 Trondheim Conference

Session

CONTRIBUTED Design of Experiment 6

28 Jun 2022, 17:35

EL6

Conveners

CONTRIBUTED Design of Experiment 6

• John Tyssedal (NTNU)

72. Tailoring DOE Constraints to the Problem

Volker Kraft (JMP)

28/06/2022, 17:35

Design and analysis of experiments

There are often constraints among the factors in experiments that are important not to to violate, but are difficult to describe in mathematical form. In this presentation, we illustrate a simple workflow of creating a simulated dataset of candidate factor values. From there, we identify a physically realisable set of potential factor combinations that is supplied to the new Candidate Set...

97. Robustness analysis in uncertainty quantification via perturbed law-based sensitivity indices

Bertrand Iooss (EDF R&D)

28/06/2022, 17:55

Design and analysis of experiments

When dealing with uncertainty quantification (UQ) in numerical simulation models, one of the most critical hypotheses is the choice of the probability distributions of the uncertain input variables which are propagated through the model. Bringing stringent justifications to these choices, especially in a safety study, requires quantifying the impact of potential uncertainty on the input...

92. SVEM: A Model Averaging Approach Capable of Fitting Supersaturated Models to Designed Experiments

Chris Gotwalt (JMP Division of SAS Institute)

Design and analysis of experiments

DoE methods have evolved over the years, as have the needs and expectations of experimenters. Historically, the focus emphasized separating effects to reduce bias in effect estimates and maximizing hypotheses testing power, which are largely a reflection of the methodological and computational tools of their time. Often in industry designed experiments are done to predict product or process...