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In this work, we developed a numerical version of the Unscented Transform. In the approach developed, any probability distributions can be mapped by linear or non-linear functions, thus enabling fast acquisition of the probability distributions of the outputs of the simulation model, or more specifically, the evaluation of model uncertainties. An appropriate degree of accuracy can be achieved, since the equation allows the arbitrary increment of sample points of mapping functions, at the expense of increased computational cost. Nevertheless, for practical purposes of mapping distributions, the computational cost is still considerably lower than that demanded, for example, by the Monte Carlo method, which is based on a massive random sampling, thus presenting a very high computational cost. Applications in problems of assessment of uncertainties in engineering show the applicability and efficiency of the proposed method.

TRANSFORMADA DA INCERTEZA PURAMENTE NUMÉRICA PARA O MAPEAMENTO DE DISTRIBUIÇÕES DE PROBABILIDADE