Heterogeneous data-driven aerodynamic modeling based on physical feature embedding
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Subject
Mechanical Engineering,Aerospace Engineering
Reference20 articles.
1. Kriging surrogate model and its application to design optimization: a review of recent progress;Han;Acta Aeronaut Astronaut Sin,2016
2. Deng K, Chen H. A hybrid aerodynamic optimization algorithm based on differential evolution and RBF response surface. In: 17th AIAA/ISSMO multidisciplinary analysis and optimization conference. 2016. p. 3671.
3. Multiple aerodynamic coefficient prediction of airfoils using a convolutional neural network;Chen;Symmetry,2020
4. Zhang Y, Sung WJ, Mavris DN. Application of convolutional neural network to predict airfoil lift coefficient. In: 2018 AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference. 2018. p. 1903.
5. Rajaram D, Puranik TG, Renganathan A, et al. Deep Gaussian process enabled surrogate models for aerodynamic flows. In: AIAA scitech 2020 forum. 2020. p. 1640.
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