A KNOWLEDGE-BASED SYSTEM FOR NUMERICAL DESIGN OF EXPERIMENTS

Numerical Designs of Experiments (DoE) can be used in a simulation process for optimization or metamodelling. A DoE may be costly, and methods are used to reduce its computational cost, as adaptive DoE. They are efficient but complex to be configured and controlled. The time saved by using these methods may be lost for the configuration step. A knowledge based-system is proposed to capitalize and reuse each DoE process configuration. An inference methodology, combining bayesian network and artificial neural network, is proposed. This system proposes improved configurations to the designer.

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