Abstract. Growing popularity of probabilistic and stochastic optimization methods in engineering applications has vastly increased the number of sampling points required to obtain a solution. Depending on the complexity of the underlying physical model, this often proves to be a computationally burdensome challenge. In order to overcome this challenge, one possible approach is to use surrogate models (metamodels), which approximate the responses of the physical model in a given variable subspace.In the past years, many different metamodeling algorithms such as Gaussian process (Kriging), moving (weighted) least squares, radial basis functions, regression neural networks and support vector regression have been suggested as an alternative to ordinary least squares (polynomial regression). The choice of the best metamodeling algorithm for any application is not a trivial task. Although there has been previous research to compare at least some of these methods to some extent ([1, 2, 3]), only a small number of publications compare all of these algorithms over a large number of multidimensional functions with varying characteristics.In this paper, a comparison of the aforementioned methods is carried out. Using well-known analytical test functions for optimization, this paper aims to shed some light on the question of which algorithm performs best under which conditions. Apart from the structure of analytical test functions, the influence of the number of sampling points and the amount of noise in the observations on the performance of metamodeling algorithms is investigated.

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