Focus of this paper is on the prediction accuracy of multidimensional functions at an inaccessible point. The paper explores the possibility of extrapolating a high-dimensional function using multiple one-dimensional converging lines. The main idea is to select samples along lines towards the inaccessible point. Multi-dimensional extrapolation is thus transformed into a series of one-dimensional extrapolations that provide multiple estimates at the inaccessible point. We demonstrate the performance of converging lines using Kriging to extrapolate a two-dimensional drag coefficient function. Post-processing of extrapolation results from different lines based on Bayesian theory is proposed to combine the multiple predictions. Selection of lines is also discussed. The method of converging lines proves to be more robust and reliable than two-dimensional Kriging surrogate for the example.

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