Implementation and Assessment of Machine Learning Methods for Multidisciplinary Aerospace Problems

In this work, we present a comparative study on machine learning-based surrogate modeling for multidisciplinary aerospace applications. The main objective of the work is to demonstrate the prediction capability of traditional machine learning methods which are random forest, gradient boosting, extreme gradient boosting, support vector machine, and multi-layer perception on multidisciplinary engineering problems. The selected datasets are determined in different complexity levels and disciplines. These methods are first applied to two benchmark functions with different mathematical characteristics and then, to four engineering design problems. The models are compared in terms of root mean squared error, mean absolute error, coefficient of determination R2 scores, and the number of hyperparameters. The obtained results reveal that the selected methods can provide accurate predictions on the presented datasets.