awesome-engineer-design
A curated list of awesome engineer design papers, including airfoil design, 3D print, CAD design
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A curated list of awesome engineer design papers, including airfoil design, 3D print, CAD design
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Created about 2 years ago
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README.md
Awesome-Engineer-Design 
A curated list of awesome engineer design papers, inspired by awesome-aigc-3d.
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Table of Contents
Survey
- Deep Generative Models in Engineering Design: A Review, Regenwetter et al., JMD 2022 | bibtex
- Machine Learning in Aerodynamic Shape Optimization, Li et al., Prog. Aerosp. Sci 2022 | bibtex
Papers
Airfoil Inverse Design
- [Synthesizing Designs With Inter-Part Dependencies Using Hierarchical Generative Adversarial Networks](https://ideal.umd.edu/assets/pdfs/chen_hgan_jmd_2019.pdf), Chen et al., JMD 2019 | [github](https://github.com/IDEALLab/hgan_jmd_2019) | [bibtex](./citations/hgan.txt) - [PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs](https://arxiv.org/abs/2002.11304), Chen et al., IDETC 2020 | [github](https://github.com/wchen459/PaDGAN) | [bibtex](./citations/padgan.txt) - [MO-PaDGAN for Design Reparameterization and Optimization](https://arxiv.org/abs/2009.07110), Chen et al., Applied Soft Computing 2021 | [github](https://github.com/wchen459/MO-PaDGAN-Optimization) | [bibtex](./citations/mo-padgan.txt) - [Data-driven design exploration method using conditional variational autoencoder for airfoil design](https://link.springer.com/article/10.1007/s00158-021-02851-0), Yonekura et al., SAMO 2021 | [bibtex](./citations/airfoil-cvae.txt) - [PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design](https://arxiv.org/abs/2106.03620), Nobari et al., SIGKDD 2021 | [github](https://github.com/pcdgan/PcDGAN) | [bibtex](./citations/pcdgan.txt) - [An inverse design method for supercritical airfoil based on conditional generative models](https://www.semanticscholar.org/paper/An-inverse-design-method-for-supercritical-airfoil-Wang-Li/e03d299d94ab436c64e07e57e6e09e913d1a22c8), Wang et al., Chinese Journal of Aeronautics 2021 | [bibtex](./citations/cvae-gan.txt) - [Generating various airfoil shapes with required lift coefficient using conditional variational autoencoders](https://arxiv.org/abs/2106.09901), Yonekura et al., EAAI 2022 | [bibtex](./citations/airfoil-cvae-lift.txt) - [Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp](https://arxiv.org/abs/2110.00212), Yonekura et al., SAMO 2022 | [bibtex](./citations/airfoil-wgan-gp.txt) - [Inverse design of two-dimensional airfoils using conditional generative models and surrogate log-likelihoods](https://asmedigitalcollection.asme.org/mechanicaldesign/article/144/2/021712/1122916), Chen et al., JMD 2022 | [bibtex](./citations/airfoil-cgan-sur.txt) - [Physics-guided training of GAN to improve accuracy in airfoil design synthesis](https://arxiv.org/abs/2308.10038), Wada et al., CMAME 2024 | [bibtex](./citations/airfoil-pgGAN.txt) - [Airfoil generation and feature extraction using the conditional VAE-WGAN-gp](https://arxiv.org/abs/2311.05445), Yonekura et al., arxiv 2023 | [bibtex](./citations/airfoil-vae-wgan-gp.txt) - [CinDM: Compositional Generative Inverse Design](https://arxiv.org/abs/2401.13171), Wu et al., ICLR 2024 | [github](https://github.com/AI4Science-WestlakeU/cindm) | [bibtex](./citations/cindm.txt) - [Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design](https://arxiv.org/abs/2402.17299), Li et al., arxiv 2024 | [bibtex](./citations/super-airfoil.txt) - [CcDPM: A Continuous Conditional Diffusion Probabilistic Model for Inverse Design](https://ojs.aaai.org/index.php/AAAI/article/view/29647), Zhao et al., AAAI 2024 | [bibtex](./citations/ccdpm.txt)Airfoil Parameterization & Shape Optimization
- [Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks](https://arc.aiaa.org/doi/10.2514/6.2019-2351), Chen et al., AIAA 2019 | [github](https://github.com/IDEALLab/airfoil-opt-gan) | [bibtex](./citations/airfoil-opt-gan.txt) - [Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks](https://arxiv.org/abs/2006.12496), Chen et al., AIAA 2020 | [github](https://github.com/IDEALLab/bezier-gan) | [bibtex](./citations/bezier-gan.txt) - [A B-Spline-based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization](https://arc.aiaa.org/doi/10.2514/6.2020-2128), Du et al., AIAA 2020 | [bibtex](./citations/bspline-gan.txt) - [CST-GANs: A Generative Adversarial Network Based on CST Parameterization for the Generation of Smooth Airfoils](https://ieeexplore.ieee.org/document/9987080), Lin et al., ICUS 2022 | [bibtex](./citations/cst-gan.txt) - [Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization](https://arxiv.org/abs/2101.04757), Wang et al., JCDE 2022 | [bibtex](./citations/airfoil-gan.txt) - [Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation](https://arxiv.org/abs/2101.02744), Chen et al., AIAA 2021 | [bibtex](./citations/airfoil-3d.txt) - [Parametric Generative Schemes with Geometric Constraints for Encoding and Synthesizing Airfoils](https://arxiv.org/abs/2205.02458), Xie et al., EAAI 2024 | [bibtex](./citations/airfoil-geo.txt) - [An Intelligent Method for Predicting the Pressure Coefficient Curve of Airfoil-Based Conditional Generative Adversarial Networks](https://ieeexplore.ieee.org/document/9547003/), Wang et al., TNNLS 2023 | [bibtex](./citations/airfoil-pressure.txt) - [Airfoil Optimization using Design-by-Morphing](https://arxiv.org/abs/2207.11448), Sheikh et al., JCDE 2023 | [bibtex](./citations/airfoil-morph.txt) - [Compact and Intuitive Airfoil Parameterization Method through Physics-aware Variational Autoencoder](https://arxiv.org/abs/2311.10921), Kang et al., arxiv 2023 | [bibtex](./citations/airfoil-pvae.txt) - [A mechanism-informed reinforcement learning framework for shape optimization of airfoils](https://arxiv.org/abs/2403.04329), Wang et al., arxiv 2024 | [bibtex](./citations/airfoil-RL.txt) - [Optimizing Diffusion to Diffuse Optimal Designs](https://arc.aiaa.org/doi/10.2514/6.2024-2013), Diniz et al., AIAA 2024 | [github](https://github.com/IDEALLab/OptimizingDiffusionSciTech2024) | [bibtex](./citations/OptimizingDiffusionSciTech2024.txt)Airfoil Editing
> TODOAirfoil aerodynamic performace prediction
> 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](https://github.com/ziliHarvey/CNN-for-Airfoil/blob/master/Report.pdf), Liu et al., CMU-course project | [github](https://github.com/ziliHarvey/CNN-for-Airfoil) - [Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method](https://pubs.aip.org/aip/pof/article-abstract/34/11/117116/2848801), Liu et al., PoF 2022 | [bibtex](./citations/predict-optimize.txt) - [An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations](https://arxiv.org/abs/2206.14709), Bonnet et al., ICLRW 2022 | [github](https://github.com/Extrality/ICLR_NACA_Dataset_V0) | [bibtex](./citations/extensible.txt) - [AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions](https://arxiv.org/abs/2212.07564), Bonnet et al., NeurIPS 2022 | [github](https://github.com/Extrality/AirfRANS) | [bibtex](./citations/airfRANS.txt) - [Fast aerodynamics prediction of laminar airfoils based on deep attention network](https://pubs.aip.org/aip/pof/article-abstract/35/3/037127/2882158), Zuo et al., PoF 2023 | [github](https://github.com/zuokuijun/vitAirfoilEncoder) | [bibtex](./citations/DAN.txt) - [A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation](https://arxiv.org/abs/2401.08705), Cao et al., PoF 2024 | [github](https://github.com/cao-wenbo/nnfoil) | [bibtex](./citations/nnfoil.txt) - [Incorporating Riemannian Geometric Features for Learning Coefficient of Pressure Distributions on Airplane Wings](https://arxiv.org/abs/2401.09452), Hu et al., arXiv 2024 | [github](https://github.com/huliwei123/Incorporating-Riemannian-Geometric-Features-for-Learning-CP-Distributions-on-Airplane-Wings) |[bibtex](./citations/incorporating.txt)CAD Design
- [BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry](https://arxiv.org/abs/2401.15563), Xu et al., SIGGRAPH 2024 | [github](https://github.com/samxuxiang/BrepGen) | [bibtex](./citations/brepGen.txt) - [TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds](https://arxiv.org/abs/2407.12702), Dupont et al., arxiv 2024 | [bibtex](./citations/TransCAD.txt) - [SolidGen: An Autoregressive Model for Direct B-rep Synthesis](https://arxiv.org/abs/2203.13944), Jayaraman etal., TMLR 2023 | [bibtex](./citations/SolidGen.txt) - [Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts](https://arxiv.org/abs/2409.17106), Mohammad et al., NeurIPS 2024 | [bibtext](./citations/text2cad.txt)Other engineer design
- [CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis](https://arxiv.org/abs/2103.06242), Nobari et al., IDETC-CIE 2021 | [github](https://github.com/mfrashad/creativegan) | [bibtex](./citations/creativegan.txt) - [Diffusion Models Beat GANs on Topology Optimization](https://arxiv.org/abs/2208.09591), Mazé et al., AAAI 2023 | [github](https://github.com/francoismaze/topodiff) | [bibtex](./citations/topodiff.txt) - [Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms](https://ieeexplore.ieee.org/document/9983478), Ding et al., TPAMI 2023 | [github](https://github.com/UBCDingXin/improved_CcGAN) | [bibtex](./citations/improved_CcGAN.txt) - [Using Graph Neural Networks for Additive Manufacturing](https://developer.nvidia.com/blog/using-graph-neural-networks-for-additive-manufacturing/), Jain et al., NVIDIA - [Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership](https://www.nature.com/articles/s41467-024-48534-4), Snapp et al., Nature Communications 2024 | [bibtex](./citations/superlative_me.txt) - [DfAM: Leveraging Generative Design in Design for Additive Manufacturing](https://mightybucket.github.io/projects/2021/05/31/masters-dissertation.html), Zhang et al. Master’s Project - [Physically Compatible 3D Object Modeling from a Single Image](https://arxiv.org/abs/2405.20510v1), Guo et al., arxiv 2024 | [bibtext](./citations/physically_3d.txt)Benchmarks and Datasets
- UIUC Airfoil data
- BigFoil
- G2Aero, Grey et al., JCDE 2023 | github | bibtex
- AFBench, Liu et al., NeurIPS 2024 | github | bibtex
Challenges
Talks
TODO
Company&Team&Experts
- Design Computation and Digital Engineering (DeCoDE) Lab, MIT |
- Design, Engineering And Learning (IDEAL) Lab, UMD | github
- Wei Chen, UMD
- Extrality
- AutoDesk
- Zoo: Building Infrastructure for Hardware Designers
Implementations
- XFoil, MIT
- AeroSandbox, Peter D. | bibtex
- adflow, Mader et al., JAIS 2020 | bibtex
- airfoil-interpolation, Chen
- Anton: generative design framework
- text-to-CAD, Zoo et al., | github
Notes
- physics-based deep learning, Thuerey et al., WWW 2021 | bibtex
- Autodesk’s AI Innovations Transforming Sustainable Design and Construction, Autodesk
License
Awesome Engineer Design is released under the MIT license.
Citation
TODO
Contact
contact: hitcslj@stu.hit.edu.cn.
Owner
- Name: Jian Liu
- Login: hitcslj
- Kind: user
- Location: Harbin, Heilongjiang, China
- Company: Harbin Institute of Technology
- Repositories: 2
- Profile: https://github.com/hitcslj
PhD Student @ HIT | Research Intern @ Megvii-reseach
Citation (citations/DAN.txt)
@article{zuo2023fast,
title={Fast aerodynamics prediction of laminar airfoils based on deep attention network},
author={Zuo, Kuijun and Ye, Zhengyin and Zhang, Weiwei and Yuan, Xianxu and Zhu, Linyang},
journal={Physics of Fluids},
volume={35},
number={3},
year={2023},
publisher={AIP Publishing}
}
GitHub Events
Total
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- Fork event: 2
Last Year
- Watch event: 10
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