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Experimental designs are tools which can dramatically reduce the number ofsimulations required by time-consuming computer codes. Because we don't knowthe true relation between the response and inputs, designs should allow one tofit a variety of models and should provide information about all portions ofthe experimental region. One strategy for selecting the values of the inputs atwhich to observe the response is to choose these values so they are spreadevenly throughout the experimental region, according to "space-fillingdesigns". In this article, we suggest a new method based on comparing theempirical distribution of the points in a design to the uniform distributionwith the Kullback-Leibler information. The considered approach consists inestimating this difference or, reciprocally, the Shannon entropy. The entropyis estimated by a Monte Carlo method where the density function is replaced byits kernel density estimator or by using the nearest neighbor distances

Plans D'Experiences D'Information De Kullback-Leibler Minimale