Improving the accuracy of surrogate models using inverse transform sampling

This paper applies inverse transform sampling to sample training points for surrogate models. Inverse transform sampling uniformly generates a sequence of real numbers ranging from 0 to 1 as the probabilities at sample points. The coordinates of the sample points are evaluated using the inverse functions of Cumulative Distribution Functions (CDF). The inputs to surrogate models are assumed to be independent random variables. The sample points obtained by inverse transform sampling can effectively represent the frequency of occurrence of the inputs. The distributions of inputs to the surrogate models are fitted to their observed data. These distributions are used for inverse transform sampling. The sample points have larger densities in the regions where the Probability Density Functions (PDF) are higher. This sampling approach ensures that the regions with higher densities of sample points are more prevalent in the observations of the random variables. Inverse transform sampling is applied to the development of surrogate models for window performance evaluation. The distributions of the following three climatic conditions are fitted: (i) the outside temperature, (ii) the wind speed, and (iii) the solar radiation. The sample climatic conditions obtained by the inverse transform sampling are used as training points to evaluate the heat transfer through a generic triple pane window. Using the simulation results at the sample points, surrogate models are developed to represent the heat transfer through the window as a function of the climatic conditions. It is observed that surrogate models developed using the inverse transform sampling can provide higher accuracy than that developed using the Sobol sequence directly for the window performance evaluation.