Multi-fidelity deep neural network surrogate model for aerodynamic shape prediction based on multi-task learning
Author:
Affiliation:
1. Shanghai University,School of Computer Engineering and Science,Shanghai,China,200444
2. State Key Laboratory of Aerodynamics, China Aerodynamics Research and Development Center,Mianyang,Sichuan,China,621000
Funder
Natural Science Foundation of Shanghai
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10624604/10624605/10624734.pdf?arnumber=10624734
Reference14 articles.
1. XFOIL: An Analysis and Design System for Low Reynolds Number Airfoils
2. PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs
3. XFOIL vs CFD performance predictions for high lift low Reynolds number airfoils
4. Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization
5. Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model
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