Copyright © 2021, The Author(s)Pérez, E. A.Periáñez, C. P.2022-02-152022-02-152021-03-10Complex and Intelligent Systems 7: 1991-2021(2021)https://link.springer.com/article/10.1007/s40747-021-00307-yhttp://hdl.handle.net/20.500.12666/613Computational fluid dynamics (CFD) simulations are nowadays been intensively used in aeronautical industries to analyse the aerodynamic performance of different aircraft configurations within a design process. These simulations allow to reduce time and cost compared to wind tunnel experiments or flight tests. However, for complex configurations, CFD simulations may still take several hours using high-performance computers to deliver results. For this reason, surrogate models are currently starting to be considered as a substitute of the CFD tool with a reasonable prediction. This paper presents a review on surrogate regression models for aerodynamic coefficient prediction, in particular for the prediction of lift and drag coefficients. To compare the behaviour of the regression models, three different aeronautical configurations have been used, a NACA0012 airfoil, a RAE2822 airfoil and 3D DPW wing. These databases are also freely provided to the scientific community to allow other researchers to make further comparison with other methods.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/Machine learningAerodynamic analysisComputational fluid dynamicsSurrogate modelingRegressionSupport vector machines for regressioninfo:eu-repo/semantics/article10.1007/s40747-021-00307-y2198-6053info:eu-repo/semantics/openAccess