Estimating sensitivities and the uncertainties associated with variable parameters can be prohibitively expensive for complex systems, particularly when sampling techniques, e.g., Monte Carlo, are employed. One approach is to define a response surface based on easy to compute functions using least squares fitting. However, such a surface does not pass through each of the data points and makes it difficult to determine the degree of interaction between the parameters of the system. Parameter interactions can be accurately determined using global sensitivity, but it is computationally expensive. Gaussian Processes can be used to create an inexpensive to evaluate response surface that is an accurate representation of the data. The paper describes the use of Gaussian Processes in conjunction with global sensitivity to examine the behavior of a thermal system, showing that the combination is an effective tool.

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