It is applied to better understand the variables affecting the design process. Sensitivity and Dependence analysis are used to filter non-informative design parameters in the study, ranking the informative ones with respect to their influence on the given response function and selecting parameters that provide the best approximation. As a result, it ensures time and resource saving on experiments and smarter decision-making based on the detailed knowledge of the design space. Adaptive DoE with Uniform, Maximum Variance and Integrated Mean Squared Errors Gain - Maximum Variance criteria.Äesign of Experiments allows controlling the process of surrogate modeling via adaptive sampling plan, which benefits the quality of approximation.Composite, D-optimal, IV-optimal, Box Behnken. Sequential techniques ( Random, Halton, Sobol, Faure sequences).Batch techniques (Random, Full-Factorial, Latin hypercube sampling, Optimal LHS).Design of ExperimentsÄesign of Experiments includes the following techniques: Design of Experiments allows exploring design space using as small number of observations as possible, enables reliable surrogate-based optimization and generates a training sample for building an accurate approximation model.
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