Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression.
The American Statistician, 46(3), 175.
https://doi.org/10.2307/2685209
Askari, R., & Soltani, M. R. (2020). Flow asymmetry in a Y-Shaped diverterless supersonic inlet: A novel finding.
AIAA Journal, 58(6), 2609–2620.
https://doi.org/10.2514/1.J059006
Aziz, N., Akhir, E. A. P., Aziz, I. A., Jaafar, J., Hasan, M. H., & Abas, A. N. C. (2020).
A study on gradient boosting algorithms for development of AI monitoring and prediction systems. 2020 International Conference on Computational Intelligence (ICCI), 11–16.
https://doi.org/10.1109/ICCI51257.2020.9247843
Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces. Wiley.
Chen, V. C. P., Tsui, K. L., Barton, R. R., & Meckesheimer, M. (2006). A review on design, modeling and applications of computer experiments.
IIE Transactions, 38(4), 273–291.
https://doi.org/10.1080/07408170500232495
Drężek, P. S., Kubacki, S., & Żółtak, J. (2022). Multi-objective surrogate model-based optimization of a small aircraft engine air-intake duct.
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236(14), 2909–2921.
https://doi.org/10.1177/09544100211070868
Erickson, C. B. (2019). Adaptive computer experiments for metamodeling. Northwestern University.
Friedman, L. W., & Pressman, I. (1988). The metamodel in simulation analysis: can it be trusted?
The Journal of the Operational Research Society, 39(10), 939.
https://doi.org/10.2307/2583045
Gabriel Pereira Gouveia da Silva, F. M. C. (2019). Neural network metamodeling for aerodynamic optimization efficiency improvement. IV Simpósio do Programa de Pós-Graduação em Engenharia Mecânica da EESC-USP (SiPGEM/EESC-USP).
Kianifar, M. R., & Campean, F. (2020). Performance evaluation of metamodelling methods for engineering problems: Towards a practitioner guide.
Structural and Multidisciplinary Optimization, 61(1), 159–186.
https://doi.org/10.1007/s00158-019-02352-1
Kim, H., Loh, W. Y., Shih, Y. S., & Chaudhuri, P. (2007). Visualizable and interpretable regression models with good prediction power.
IIE Transactions, 39(6), 565–579.
https://doi.org/10.1080/07408170600897502
Kleijnen, J. P. C. (2017). Regression and kriging metamodels with their experimental designs in simulation: A review.
European Journal of Operational Research, 256(1), 1–16.
https://doi.org/10.1016/j.ejor.2016.06.041
Kyprioti, A. P., Zhang, J., & Taflanidis, A. A. (2020). Adaptive design of experiments for global Kriging metamodeling through cross-validation information.
Structural and Multidisciplinary Optimization, 62(3), 1135–1157.
https://doi.org/10.1007/s00158-020-02543-1
Le Clainche, S., Ferrer, E., Gibson, S., Cross, E., Parente, A., & Vinuesa, R. (2023). Improving aircraft performance using machine learning: A review.
Aerospace Science and Technology, 138, 108354.
https://doi.org/10.1016/j.ast.2023.108354
Li, Y. F., Ng, S. H., Xie, M., & Goh, T. N. (2010). A systematic comparison of metamodeling techniques for simulation optimization in decision support systems.
Applied Soft Computing, 10(4), 1257–1273.
https://doi.org/10.1016/j.asoc.2009.11.034
Patel, T., Singh, S. N., & Seshadri, V. (2005). Characteristics of Y-Shaped rectangular diffusing duct at different inflow conditions
. Journal of Aircraft, 42(1), 113–120.
https://doi.org/10.2514/1.4690
Pearson, K. (1895). Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London Series I, 58, 240–242.
Poggi, C., Rossetti, M., Serafini, J., Bernardini, G., Gennaretti, M., & Iemma, U. (2022). Neural network meta–modelling for an efficient prediction of propeller array acoustic signature.
Aerospace Science and Technology, 130, 107910.
https://doi.org/10.1016/j.ast.2022.107910
Seddon, J., & Goldsmith, E. L. (1999). Intake Aerodynamics. 2nd ed., AIAA Education Series, American Institute of Aeronautics and Astronautics.
Simpson, T. W., Poplinski, J. D., Koch, P. N., & Allen, J. K. (2001). Metamodels for computer-based engineering design: Survey and recommendations.
Engineering with Computers, 17(2), 129–150.
https://doi.org/10.1007/PL00007198
Singh, U., Rizwan, M., Alaraj, M., & Alsaidan, I. (2021). A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments.
Energies, 14(16), 5196.
https://doi.org/10.3390/en14165196
Sun, G., & Wang, S. (2019). A review of the artificial neural network surrogate modeling in aerodynamic design.
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(16), 5863–5872.
https://doi.org/10.1177/0954410019864485
Van Gelder, L., Das, P., Janssen, H., & Roels, S. (2014). Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners.
Simulation Modelling Practice and Theory, 49, 245–257.
https://doi.org/10.1016/j.simpat.2014.10.004
Wang, H., Shan, S., Wang, G. G., & Li, G. (2011). Integrating least square support vector regression and mode pursuing sampling optimization for crashworthiness design.
Journal of Mechanical Design, 133(4), 041002.
https://doi.org/10.1115/1.4003840
Wu, H., Zhao, Y. P., & Hui-Jun, T. (2022). A hybrid of fast K-nearest neighbor and improved directed acyclic graph support vector machine for large-scale supersonic inlet flow pattern recognition.
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236(1), 109–122.
https://doi.org/10.1177/09544100211008601
Yang, R. J., Gu, L., Liaw, L., Gearhart, C., Tho, C. H., Liu, X., & Wang, B. P. (2000).
Approximations for safety optimization of large systems. 26th Design Automation Conference, 763–772.
https://doi.org/10.1115/DETC2000/DAC-14245
Yang, T., Zhiyong, L., Neng, X., Yan, S., & Jun, L. (2018). Optimization of positional parameters of close-formation flight for blended-wing-body configuration.
Heliyon, 4(12), e01019.
https://doi.org/10.1016/j.heliyon.2018.e01019
Zan, B. W., Han, Z. H., Xu, C. Z., Liu, M. Q., & Wang, W. Z. (2022). High-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network.
Advances in Aerodynamics, 4(1), 39.
https://doi.org/10.1186/s42774-022-00128-8