## 1 Introduction  
One important step in shape optimization is constructing a surrogate model, allowing us to predict output values as a function of input design variables. The response surface method (RSM) is a conventional way to explore the relationship between input and output variables, where the output variables are approximated by a form of polynomial function with the input design variables [1, 2]. The degree of the polynomial can be set by users, but a second-order polynomial works well, as shown in [PERSON] and [PERSON] [3]. Using an artificial neural network (ANN) is another way to construct a surrogate model [4, 5, 6]. Neuron networks here consist of input, hidden, and output layers, and each layer has its own neurons with their own bias. Neurons in one layer are connected to the neurons in the next layer, and the strength of associations between two-connected neurons is measured by weights. Using the datasets obtained from the design of experiment, neuron networks are trained to find the best values of weights and biases that minimize the discrepancy between \"real\" output values and \"trained\" output values (output values computed from an ANN). Several factors influence the accuracy of an ANN's prediction such as the number of hidden layers, the number of neurons in each hidden layer, and the number of training datasets, but an ANN usually provides a high accurate approximation, even when the relation between the input and output variables is complicated [7]. In the present study, the prediction accuracies of the surrogate models constructed using a RSM and ANN are compared in predicting the aerodynamic performance of a wind turbine airfoil. This is performed for two reasons. First, wind is a good source for converting economical and ecofriendly energy, so it has great significance as a form of renewable energy. In fact, according to U.S. Energy Information Administration, over 20% of renewable energy in the U.S. was provided by wind energy in 2018 [8]. The recent development of various types of wind turbines such as wind turbines with dimples [9; 10] and Darrieus vertical wind turbines [11; 12] broadens the usage of wind energy by overcoming previous simple horizontal wind turbines' drawbacks such as noise and spatial limitation, leading to the expectation that more and more energy will be produced by wind in the future [13; 14; 15]. Second, a wind turbine airfoil shape and its operating conditions significantly affect the magnitude of the aerodynamic force acting on the wind turbine, and therefore, optimizing them is important. Several researchers have studied the development of high-performance wind turbine airfoil shapes to improve the overall efficiency in the conversion of wind energy. For example, [PERSON] et al. [16] developed a new method called the singularities method to analyze and improve the performance of a wind turbine's airfoil profile. [PERSON] et al. [17] modified the DU91-W2-250 wind turbine airfoil by creating various small flaps such as Gurney flaps and trailing edge wedges, and investigated those flaps' effects on the lift and drag coefficient of the airfoil. Similar efforts to improve wind turbine airfoil by modifying the shapes of the wind turbine at the trailing edge have been performed in [PERSON] and [PERSON] [18], [PERSON] et al. [19], [PERSON] et al. [20], and [PERSON] et al. [21]. The effect of a dimple on the wind turbine airfoil on its efficiency is studied in [PERSON] et al. [22], [PERSON] and [PERSON] [10], and [PERSON] and [PERSON] [23]. The studies presented in [PERSON] et al. [24] and [PERSON] and [PERSON] [25] investigated the influence of wind turbine's operating conditions such as Reynolds number and angle of attack on its aerodynamic performance, and the optimal designs of wind turbine airfoils are found at different operating conditions in [PERSON] et al. [26] and [PERSON] and [PERSON] [25].