• Synthesizing Designs With Inter-Part Dependencies Using Hierarchical Generative Adversarial Networks, Chen et al., JMD 2019 | github | bibtex
• PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs, Chen et al., IDETC 2020 | github | bibtex
• MO-PaDGAN for Design Reparameterization and Optimization, Chen et al., Applied Soft Computing 2021 | github | bibtex
• Data-driven design exploration method using conditional variational autoencoder for airfoil design, Yonekura et al., SAMO 2021 | bibtex
• PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design, Nobari et al., SIGKDD 2021 | github | bibtex
• An inverse design method for supercritical airfoil based on conditional generative models, Wang et al., Chinese Journal of Aeronautics 2021 | bibtex
• Generating various airfoil shapes with required lift coefficient using conditional variational autoencoders, Yonekura et al., EAAI 2022 | bibtex
• Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp, Yonekura et al., SAMO 2022 | bibtex
• Inverse design of two-dimensional airfoils using conditional generative models and surrogate log-likelihoods, Chen et al., JMD 2022 | bibtex
• Physics-guided training of GAN to improve accuracy in airfoil design synthesis, Wada et al., CMAME 2024 | bibtex
• Airfoil generation and feature extraction using the conditional VAE-WGAN-gp, Yonekura et al., arxiv 2023 | bibtex
• CinDM: Compositional Generative Inverse Design, Wu et al., ICLR 2024 | github | bibtex
• Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design, Li et al., arxiv 2024 | bibtex
• CcDPM: A Continuous Conditional Diffusion Probabilistic Model for Inverse Design, Zhao et al., AAAI 2024 | bibtex

• Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks, Chen et al., AIAA 2019 | github | bibtex
• Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks, Chen et al., AIAA 2020 | github | bibtex
• A B-Spline-based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization, Du et al., AIAA 2020 | bibtex
• CST-GANs: A Generative Adversarial Network Based on CST Parameterization for the Generation of Smooth Airfoils, Lin et al., ICUS 2022 | bibtex
• Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization, Wang et al., JCDE 2022 | bibtex
• Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation, Chen et al., AIAA 2021 | bibtex
• Parametric Generative Schemes with Geometric Constraints for Encoding and Synthesizing Airfoils, Xie et al., EAAI 2024 | bibtex
• An Intelligent Method for Predicting the Pressure Coefficient Curve of Airfoil-Based Conditional Generative Adversarial Networks, Wang et al., TNNLS 2023 | bibtex
• Airfoil Optimization using Design-by-Morphing, Sheikh et al., JCDE 2023 | bibtex
• Compact and Intuitive Airfoil Parameterization Method through Physics-aware Variational Autoencoder, Kang et al., arxiv 2023 | bibtex
• A mechanism-informed reinforcement learning framework for shape optimization of airfoils, Wang et al., arxiv 2024 | bibtex
• Optimizing Diffusion to Diffuse Optimal Designs, Diniz et al., AIAA 2024 | github | bibtex

TODO

Based on the solution approach, the methods can be divided into PINNs (Neural Networks for solving equations) and data-driven surrogate models. The latter can be further categorized based on the type of output: direct output of Cl/Cd (similar to classification) or output of the flow field around the airfoil (dense prediction, similar to segmentation).

• An Airfoil Aerodynamic Parameters Calculation Method Based on Convolutional Neural Network, Liu et al., CMU-course project | github
• Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method, Liu et al., PoF 2022 | bibtex
• An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations, Bonnet et al., ICLRW 2022 | github | bibtex
• AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions, Bonnet et al., NeurIPS 2022 | github | bibtex
• Fast aerodynamics prediction of laminar airfoils based on deep attention network, Zuo et al., PoF 2023 | github | bibtex
• A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation, Cao et al., PoF 2024 | github | bibtex
• Incorporating Riemannian Geometric Features for Learning Coefficient of Pressure Distributions on Airplane Wings, Hu et al., arXiv 2024 | github |bibtex

• BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry, Xu et al., SIGGRAPH 2024 | github | bibtex
• TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds, Dupont et al., arxiv 2024 | bibtex
• SolidGen: An Autoregressive Model for Direct B-rep Synthesis, Jayaraman etal., TMLR 2023 | bibtex
• Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts, Mohammad et al., NeurIPS 2024 | bibtext

• CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis, Nobari et al., IDETC-CIE 2021 | github | bibtex
• Diffusion Models Beat GANs on Topology Optimization, Mazé et al., AAAI 2023 | github | bibtex
• Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms, Ding et al., TPAMI 2023 | github | bibtex
• Using Graph Neural Networks for Additive Manufacturing, Jain et al., NVIDIA
• Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership, Snapp et al., Nature Communications 2024 | bibtex
• DfAM: Leveraging Generative Design in Design for Additive Manufacturing, Zhang et al. Master’s Project
• Physically Compatible 3D Object Modeling from a Single Image, Guo et al., arxiv 2024 | bibtext