Efficient algorithms for computing the L2-discrepancy
Mathematics of Computation
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Mathematics of Computation
Formulation of the Audze--Eglais uniform Latin hypercube design of experiments
Advances in Engineering Software
Maximin Latin Hypercube Designs in Two Dimensions
Operations Research
Bounds for Maximin Latin Hypercube Designs
Operations Research
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A widely used strategy to explore the sensitivity of the model to its inputs is based on a finite set of simulations. These are usually performed for a chosen set of points in a parameter space. An estimate of the sensitivity can be then obtained by computing correlations between the model inputs and outputs. The accuracy of the sensitivity prediction depends on a quality of the points distribution in the parameter space, so-called the design of experiments. The aim of the presented paper is to review and compare available criteria determining an optimal design of experiments for sampling-based sensitivity analysis.