## 1 Introduction
Among these tools is the open-source OpenFAST aeroelastic analysis tool developed by the National Renewable Energy Laboratory (NREL) [12]. This tool is openly available for development and usage by researchers and industry, and it uses the blade element momentum (BEM) theory for the aerodynamic loads. In contrast, the software uses the Euler beam theory for structural behavior. In addition to the low computational cost of OpenFAST, one of its major advantages is being open-source for developers to add or adjust its existing modules [13]. OpenFAST has been proven to be a reliable tool for simulating different configurations of wind turbines under different working conditions. [PERSON] et al. investigated the dynamic stall effects on the load predictions and responses of an offshore wind turbine. They implemented a novel dynamic stall model into the OpenFAST software and compared their results to the Beddoes-Leishman (B-L) model and experimental data. Their model could capture the aerodynamic coefficients corresponding to different working conditions accurately [14]. [PERSON] et al. studied the instabilities of floating offshore wind turbines under extreme wind conditions using a linearized OpenFAST module. Their developed module could accurately identify the blade's edgewise damping under different operating control parameters and extreme winds [15]. Control strategies can also be modeled and analyzed in OpenFAST. [PERSON] et al. performed a comparative study between baseline control of OpenFAST to adaptive super-twisting (STW) control methods. Their STW control effectively maximized the turbine's power output [16]. [PERSON] et al. made an aero-hydro-servo-elastic coupling analysis on a 15 MW wind turbine using OpenFAST to study the effect of yaw error and fault conditions on the dynamics of the turbine [17]. OpenFAST has gained the confidence of users in its effectiveness and accuracy for aeroelastic simulations of wind turbines over the years since its earlier versions named FAST. Simulation results made by OpenFAST have been used by many researchers as a verification case. [PERSON] et al. verified their root strain measurements for blade force estimation with OpenFAST results [18]. [PERSON] et al. developed a multibody tool named TorqTwin to model the turbine structural dynamics. They used the ElastoDyn module in OpenFAST as a reference for verification of their tool [19]. Based on the confidence level in OpenFAST simulation results, its generated data can be used to build data-based models for predicting different turbine dynamic outputs. Artificial intelligence and data-based models are used widely in renewable energy applications. Predictions of power outputs of different renewable energy systems have been employed by many researchers [20; 21]. Specifically in wind energy applications, machine learning (ML) algorithms are used to forecast wind power or to predict power outputs and dynamic loads and optimize the design parameters of a wind turbine [22; 23]. The machine and deep learning approaches widely studied in the literature are based on historical data. The models are trained on an existing experiment or simulation-based dataset and are used to predict the desired outputs of each case. The models are then tested on a portion of the existing dataset. However, to the author's knowledge, there has been no study in the literature where the ML models are tested for generalization under different conditions than those on which it has been trained.