Unsteady Aerodynamic Prediction Using Limited Samples Based on Transfer Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3998-1_81
Reference14 articles.
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2. Hu, L., Zhang, J., Xiang, Y., et al.: Neural networks-based aerodynamic data modeling: a comprehensive review. IEEE Access 8, 90805–90823 (2020). https://doi.org/10.1109/ACCESS.2020.2993562
3. Li, K., Kou, J., Zhang, W.: Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils. Aerosp. Sci. Technol. 119, 107173 (2021). https://doi.org/10.1016/j.ast.2021.107173
4. Wu, Y., Dai, Y., Yang, C., et al.: Unsteady and nonlinear aerodynamic prediction of airfoil undergoing large-amplitude pitching oscillation based on gated recurrent unit network. PI Mech. Eng. G-J Aer. 237(2), 270–284 (2023). https://doi.org/10.1177/09544100221097521
5. Alkhedher, M.: Comparative study of machine learning modeling for unsteady aerodynamics. CMC-Comput. Mater. Continua 72(1), 1901–1920 (2022). https://doi.org/10.32604/cmc.2022.025334
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