Generating various airfoils with required lift coefficients by combining NACA and Joukowski airfoils using conditional variational autoencoders

Abadi, 2015 Abbot, Ira H., von Doenhoff, Albert E., Stivers, Louis, Jr., 1945. Summary of Airfoil Data. United States. Achour, Gabriel, Sung, Woong Je, Pinon-Fischer, Olivia J., Mavris, Dimitri N., 2020. Development of a Conditional Generative Adversarial Network for Airfoil Shape Optimization, page 2261. Bonaiuti, 2010, Parametric design of a waterjet pump by means of inverse design, cfd calculations and experimental analyses, J. Fluids Eng., 132, 10.1115/1.4001005 Brown, 2019, Design variable analysis and generation for performance-based parametric modeling in architecture, Int. J. Archit. Comput., 17, 36 Bui-Thanh, 2004, Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition, AIAA J., 42, 1505, 10.2514/1.2159 Cao, 2005, Hydrodynamic design of rotodynamic pump impeller for multiphase pumping by combined approach of inverse design and cfd analysis, J. Fluids Eng., 172, 330, 10.1115/1.1881697 Chen, 2021 Davidson, 2018, Hyperspherical variational auto-encoders Drela, 1989, Xfoil: an analysis and design system for low reynolds number airfoils, vol. 54, 1 Gaggero, 2019, A reduced order approach for optimal design of efficient marine propellers, Ships Offshore Struct., 0, 1 Goto, 2002, Hydrodynamic design of pump diffuser using inverse design method and cfd, J. Fluids Eng., 124, 319, 10.1115/1.1467599 Gulrajani, 2017, Improved training of wasserstein gans Hasnat, Md. Abul, Bohne, Julien, Milgram, Jonathan, Gentric, Stephane, Chen, Liming, 2017. von Mises-Fisher mixture model-based deep learning: Application to face verification. arXiv:1706.04264v2. Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 Kennon, 1985, vol. 3 Kingma, Diederik P., Welling, Max, 2013. Auto-encoding variational bayes. arXiv:1312.6114. Lee, 2010, System reliability-based design optimization using the mpp-based dimension reduction method, Comput. Methods Appl. Mech. Eng., 41, 823 Li, 2020, Designing phononic crystal with anticipated band gap through a deep learning based data-driven method, Struct. Multidiscip. Optim., 361 Mirza, 2014 Narducci, 1995, Sensitivity algorithms for an inverse design problem involving a shock wave, Inverse Probl. Eng., 2, 49, 10.1080/174159795088027593 Nita, 2014, Film cooling hole shape optimization using proper orthogonal decomposition, GT2014 Nita, 2017 Pu, 2016, Variational autoencoder for deep learning of images, labels and captions, 2352 Sohn, 2015, Learning structured output representation using deep conditional generative models, 3483 Song, 2019, Latent space expanded variational autoencoder for sentence generation, IEEE Access, 7, 144618, 10.1109/ACCESS.2019.2944630 Tan, 2020, A deep learning-based method for the design of microstructural materials, Struct. Multidiscip. Optim., 61, 1417, 10.1007/s00158-019-02424-2 Tolstikhin, Ilya, Bousquet, Olivier, Gelly, Sylvain, Schoelkopf, Bernhard, 2017. Wasserstein auto-encoders. arXiv:1711.01558. Tortorelli, 1994, Design sensitivity analysis: overview and review, Inverse Probl. Eng., 1, 71, 10.1080/174159794088027573 Xu, 2018, Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, 187 Yonekura, 2019, Framework for design optimization using deep reinforcement learning, Struct. Multidiscip. Optim., 60, 1709, 10.1007/s00158-019-02276-w Yonekura, 2021 Yonekura, 2021, Data-driven design exploration method using conditional variational autoencoder for airfoil design, Struct. Multidiscip. Optim., 64, 613, 10.1007/s00158-021-02851-0 Yonekura, 2014, A shape parameterization method using principal component analysis in application to shape optimization, J. Mech. Des., 136, 121401, 10.1115/1.4028273 Yu, 2019, Deep learning for determining a near-optimal topological design without any iteration, Struct. Multidiscip. Optim., 59, 787, 10.1007/s00158-018-2101-5 Zhang, 2019, Learning latent representations for style control and transfer in end-to-end speech synthesis, 6945 Zhang, 2019, Deep learning-based inverse method for layout design, Struct. Multidiscip. Optim., 60, 527, 10.1007/s00158-019-02222-w Zhengming, 1985, Inverse design calculations for transonic cascades