├── .gitignore ├── paperReading ├── BrepGen.md └── TransCAD.md ├── asset └── xfoil.png ├── citations ├── aerosandbox.txt ├── topodiff.txt ├── airfoil-3d.txt ├── airfoil-vae-wgan-gp.txt ├── pbdl.txt ├── airfoil-gan.txt ├── airfoil-pvae.txt ├── OptimizingDiffusionSciTech2024.txt ├── airfoil-wgan-gp.txt ├── ml-aso.txt ├── padgan.txt ├── incorporating.txt ├── airfoil-RL.txt ├── super-airfoil.txt ├── mo-padgan.txt ├── DAN.txt ├── nnfoil.txt ├── pcdgan.txt ├── airfoil-opt-gan.txt ├── airfoil-pgGAN.txt ├── physically_3d.txt ├── cvae-gan.txt ├── deepED.txt ├── cindm.txt ├── adflow.txt ├── predict-optimize.txt ├── airfoil-morph.txt ├── ccdpm.txt ├── hgan.txt ├── bezier-gan.txt ├── bspline-gan.txt ├── extensible.txt ├── g2aero.txt ├── TransCAD.txt ├── SolidGen.txt ├── airfRANS.txt ├── text2cad.txt ├── afbench.txt ├── superlative_me.txt ├── cst-gan.txt ├── creativegan.txt ├── airfoil-cvae-lift.txt ├── airfoil-pressure.txt ├── improved_CcGAN.txt ├── airfoil-cvae.txt ├── airfoil-cgan-gp.txt └── airfoil-geo.txt ├── how-to-PR.md ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store -------------------------------------------------------------------------------- /paperReading/BrepGen.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /paperReading/TransCAD.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /asset/xfoil.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hitcslj/Awesome-Engineer-Design/HEAD/asset/xfoil.png -------------------------------------------------------------------------------- /citations/aerosandbox.txt: -------------------------------------------------------------------------------- 1 | @mastersthesis{aerosandbox, 2 | title = {AeroSandbox: A Differentiable Framework for Aircraft Design Optimization}, 3 | author = {Sharpe, Peter D.}, 4 | school = {Massachusetts Institute of Technology}, 5 | year = {2021} 6 | } -------------------------------------------------------------------------------- /citations/topodiff.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{maze2022, 2 |     author={François Mazé and Faez Ahmed}, 3 |     title={Diffusion Models Beat GANs on Topology Optimization}, 4 |     booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, 5 |    year={2023}, 6 | } -------------------------------------------------------------------------------- /citations/airfoil-3d.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{chen2021deep, 2 | title={Deep generative model for efficient 3D airfoil parameterization and generation}, 3 | author={Chen, Wei and Ramamurthy, Arun}, 4 | booktitle={AIAA Scitech 2021 Forum}, 5 | pages={1690}, 6 | year={2021} 7 | } -------------------------------------------------------------------------------- /citations/airfoil-vae-wgan-gp.txt: -------------------------------------------------------------------------------- 1 | @article{yonekura2023airfoil, 2 | title={Airfoil generation and feature extraction using the conditional VAE-WGAN-gp}, 3 | author={Yonekura, Kazuo and Tomori, Yuki and Suzuki, Katsuyuki}, 4 | journal={arXiv preprint arXiv:2311.05445}, 5 | year={2023} 6 | } -------------------------------------------------------------------------------- /citations/pbdl.txt: -------------------------------------------------------------------------------- 1 | @book{thuerey2021pbdl, 2 | title={Physics-based Deep Learning}, 3 | author={Nils Thuerey and Philipp Holl and Maximilian Mueller and Patrick Schnell and Felix Trost and Kiwon Um}, 4 | url={https://physicsbaseddeeplearning.org}, 5 | year={2021}, 6 | publisher={WWW} 7 | } -------------------------------------------------------------------------------- /citations/airfoil-gan.txt: -------------------------------------------------------------------------------- 1 | @article{wang2021airfoil, 2 | title={Airfoil GAN: Encoding and synthesizing airfoils for aerodynamic-aware shape optimization}, 3 | author={Wang, Yuyang and Shimada, Kenji and Farimani, Amir Barati}, 4 | journal={arXiv preprint arXiv:2101.04757}, 5 | year={2021} 6 | } -------------------------------------------------------------------------------- /citations/airfoil-pvae.txt: -------------------------------------------------------------------------------- 1 | @article{kang2023compact, 2 | title={Compact and Intuitive Airfoil Parameterization Method through Physics-aware Variational Autoencoder}, 3 | author={Kang, Yu-Eop and Lee, Dawoon and Yee, Kwanjung}, 4 | journal={arXiv preprint arXiv:2311.10921}, 5 | year={2023} 6 | } -------------------------------------------------------------------------------- /citations/OptimizingDiffusionSciTech2024.txt: -------------------------------------------------------------------------------- 1 | @article{Diniz2024OptimizingDT, 2 | title={Optimizing Diffusion to Diffuse Optimal Designs}, 3 | author={Cashen Diniz and Mark D. Fuge}, 4 | journal={AIAA SCITECH 2024 Forum}, 5 | year={2024}, 6 | url={https://api.semanticscholar.org/CorpusID:267324257} 7 | } -------------------------------------------------------------------------------- /citations/airfoil-wgan-gp.txt: -------------------------------------------------------------------------------- 1 | @article{yonekura2021inverse, 2 | title={Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp}, 3 | author={Yonekura, Kazuo and Miyamoto, Nozomu and Suzuki, Katsuyuki}, 4 | journal={arXiv preprint arXiv:2110.00212}, 5 | year={2021} 6 | } -------------------------------------------------------------------------------- /citations/ml-aso.txt: -------------------------------------------------------------------------------- 1 | @article{li2022machine, 2 | title={Machine learning in aerodynamic shape optimization}, 3 | author={Li, Jichao and Du, Xiaosong and Martins, Joaquim RRA}, 4 | journal={Progress in Aerospace Sciences}, 5 | volume={134}, 6 | pages={100849}, 7 | year={2022}, 8 | publisher={Elsevier} 9 | } -------------------------------------------------------------------------------- /citations/padgan.txt: -------------------------------------------------------------------------------- 1 | @article{chen2020padgan, 2 | title={PaDGAN: Learning to Generate High-Quality Novel Designs}, 3 | author={Chen, Wei and Ahmed, Faez}, 4 | journal={Journal of Mechanical Design}, 5 | volume={143}, 6 | number={3}, 7 | publisher={American Society of Mechanical Engineers Digital Collection} 8 | } -------------------------------------------------------------------------------- /citations/incorporating.txt: -------------------------------------------------------------------------------- 1 | @article{hu2023incorporating, 2 | title={Incorporating Riemannian Geometric Features for Learning Coefficient of Pressure Distributions on Airplane Wings}, 3 | author={Hu, Liwei and Wang, Wenyong and Xiang, Yu and Sommer, Stefan}, 4 | journal={arXiv preprint arXiv:2401.09452}, 5 | year={2023} 6 | } -------------------------------------------------------------------------------- /citations/airfoil-RL.txt: -------------------------------------------------------------------------------- 1 | @misc{wang2024mechanisminformed, 2 | title={A mechanism-informed reinforcement learning framework for shape optimization of airfoils}, 3 | author={Jingfeng Wang and Guanghui Hu}, 4 | year={2024}, 5 | eprint={2403.04329}, 6 | archivePrefix={arXiv}, 7 | primaryClass={math.NA} 8 | } -------------------------------------------------------------------------------- /citations/super-airfoil.txt: -------------------------------------------------------------------------------- 1 | @misc{li2024meshagnostic, 2 | title={Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design}, 3 | author={Runze Li and Yufei Zhang and Haixin Chen}, 4 | year={2024}, 5 | eprint={2402.17299}, 6 | archivePrefix={arXiv}, 7 | primaryClass={physics.flu-dyn} 8 | } -------------------------------------------------------------------------------- /citations/mo-padgan.txt: -------------------------------------------------------------------------------- 1 | @article{chen2021mo, 2 | title={MO-PaDGAN: Reparameterizing Engineering Designs for augmented multi-objective optimization}, 3 | author={Chen, Wei and Ahmed, Faez}, 4 | journal={Applied Soft Computing}, 5 | volume={113}, 6 | pages={107909}, 7 | year={2021}, 8 | publisher={Elsevier} 9 | } -------------------------------------------------------------------------------- /citations/DAN.txt: -------------------------------------------------------------------------------- 1 | @article{zuo2023fast, 2 | title={Fast aerodynamics prediction of laminar airfoils based on deep attention network}, 3 | author={Zuo, Kuijun and Ye, Zhengyin and Zhang, Weiwei and Yuan, Xianxu and Zhu, Linyang}, 4 | journal={Physics of Fluids}, 5 | volume={35}, 6 | number={3}, 7 | year={2023}, 8 | publisher={AIP Publishing} 9 | } -------------------------------------------------------------------------------- /citations/nnfoil.txt: -------------------------------------------------------------------------------- 1 | @article{cao2024solver, 2 | title={A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation}, 3 | author={Cao, Wenbo and Song, Jiahao and Zhang, Weiwei}, 4 | journal={Physics of Fluids}, 5 | volume={36}, 6 | number={2}, 7 | year={2024}, 8 | publisher={AIP Publishing} 9 | } -------------------------------------------------------------------------------- /citations/pcdgan.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{heyrani2021pcdgan, 2 | title={Pcdgan: A continuous conditional diverse generative adversarial network for inverse design}, 3 | author={Heyrani Nobari, Amin and Chen, Wei and Ahmed, Faez}, 4 | booktitle={Proceedings of the 27th ACM SIGKDD conference on knowledge discovery \& data mining}, 5 | pages={606--616}, 6 | year={2021} 7 | } -------------------------------------------------------------------------------- /citations/airfoil-opt-gan.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{chen2019aerodynamic, 2 | author={Chen, Wei and Chiu, Kevin and Fuge, Mark}, 3 | title={Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks}, 4 | booktitle={AIAA SciTech Forum}, 5 | year={2019}, 6 | month={Jan}, 7 | publisher={AIAA}, 8 | address={San Diego, USA} 9 | } -------------------------------------------------------------------------------- /how-to-PR.md: -------------------------------------------------------------------------------- 1 | 1. Put the bibtex in `awesome-airfoil-design/citations/.txt`. 2 | 2. Modify the `README.md` and follow the format TITLE, AUTHOR, CONFERENCE YEAR | OPTIONAL LINK | BIBTEX. For example: 3 | - CinDM: Compositional Generative Inverse Design 4 | , Wu et al., ICLR 2024 | [github](https://github.com/AI4Science-WestlakeU/cindm) | [bibtex](./citations/cindm.txt) -------------------------------------------------------------------------------- /citations/airfoil-pgGAN.txt: -------------------------------------------------------------------------------- 1 | @article{wada2024physics, 2 | title={Physics-guided training of GAN to improve accuracy in airfoil design synthesis}, 3 | author={Wada, Kazunari and Suzuki, Katsuyuki and Yonekura, Kazuo}, 4 | journal={Computer Methods in Applied Mechanics and Engineering}, 5 | volume={421}, 6 | pages={116746}, 7 | year={2024}, 8 | publisher={Elsevier} 9 | } -------------------------------------------------------------------------------- /citations/physically_3d.txt: -------------------------------------------------------------------------------- 1 | @article{guo2024physically, 2 | title={Physically Compatible 3D Object Modeling from a Single Image}, 3 | author={Guo, Minghao and Wang, Bohan and Ma, Pingchuan and Zhang, Tianyuan and Owens, Crystal Elaine and Gan, Chuang and Tenenbaum, Joshua B and He, Kaiming and Matusik, Wojciech}, 4 | journal={arXiv preprint arXiv:2405.20510}, 5 | year={2024} 6 | } -------------------------------------------------------------------------------- /citations/cvae-gan.txt: -------------------------------------------------------------------------------- 1 | @article{Wang2021AnID, 2 | title={An inverse design method for supercritical airfoil based on conditional generative models}, 3 | author={Jing Wang and Runze Li and Cheng He and Haixin Chen and Ran Cheng and Chen Zhai and Miao Zhang}, 4 | journal={Chinese Journal of Aeronautics}, 5 | year={2021}, 6 | url={https://api.semanticscholar.org/CorpusID:233640024} 7 | } -------------------------------------------------------------------------------- /citations/deepED.txt: -------------------------------------------------------------------------------- 1 | @article{regenwetter2022deep, 2 | title={Deep generative models in engineering design: A review}, 3 | author={Regenwetter, Lyle and Nobari, Amin Heyrani and Ahmed, Faez}, 4 | journal={Journal of Mechanical Design}, 5 | volume={144}, 6 | number={7}, 7 | pages={071704}, 8 | year={2022}, 9 | publisher={American Society of Mechanical Engineers} 10 | } -------------------------------------------------------------------------------- /citations/cindm.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{wu2024compositional, 2 | title={Compositional Generative Inverse Design}, 3 | author={Tailin Wu and Takashi Maruyama and Long Wei and Tao Zhang and Yilun Du and Gianluca Iaccarino and Jure Leskovec}, 4 | booktitle={The Twelfth International Conference on Learning Representations}, 5 | year={2024}, 6 | url={https://openreview.net/forum?id=wmX0CqFSd7} 7 | } -------------------------------------------------------------------------------- /citations/adflow.txt: -------------------------------------------------------------------------------- 1 | @article{Mader2020a, 2 | author = {Charles A. Mader and Gaetan K. W. Kenway and Anil Yildirim and Joaquim R. R. A. Martins}, 3 | doi = {10.2514/1.I010796}, 4 | journal = {Journal of Aerospace Information Systems}, 5 | title = {{ADflow}---An open-source computational fluid dynamics solver for aerodynamic and multidisciplinary optimization}, 6 | year = {2020} 7 | } -------------------------------------------------------------------------------- /citations/predict-optimize.txt: -------------------------------------------------------------------------------- 1 | @article{Liu2022PredictionAO, 2 | title={Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method}, 3 | author={Ruo-Lin Liu and Yue Hua and Zhi Zhou and Yubai Li and Wei-Tao Wu and Nadine Aubry}, 4 | journal={Physics of Fluids}, 5 | year={2022}, 6 | url={https://api.semanticscholar.org/CorpusID:253475311} 7 | } -------------------------------------------------------------------------------- /citations/airfoil-morph.txt: -------------------------------------------------------------------------------- 1 | @article{sheikh2023airfoil, 2 | title={Airfoil optimization using design-by-morphing}, 3 | author={Sheikh, Haris Moazam and Lee, Sangjoon and Wang, Jinge and Marcus, Philip S}, 4 | journal={Journal of Computational Design and Engineering}, 5 | volume={10}, 6 | number={4}, 7 | pages={1443--1459}, 8 | year={2023}, 9 | publisher={Oxford University Press} 10 | } -------------------------------------------------------------------------------- /citations/ccdpm.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{Zhao2024CcDPMAC, 2 | title={CcDPM: A Continuous Conditional Diffusion Probabilistic Model for Inverse Design}, 3 | author={Yanxuan Zhao and Peng Zhang and Guopeng Sun and Zhigong Yang and Jianqiang Chen and Yueqing Wang}, 4 | booktitle={AAAI Conference on Artificial Intelligence}, 5 | year={2024}, 6 | url={https://api.semanticscholar.org/CorpusID:268708225} 7 | } -------------------------------------------------------------------------------- /citations/hgan.txt: -------------------------------------------------------------------------------- 1 | @article{chen2019hgan, 2 | author={Chen, Wei and Fuge, Mark}, 3 | title={Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks}, 4 | journal={Journal of Mechanical Design}, 5 | volume={141}, 6 | number={11}, 7 | pages={111403}, 8 | year={2019}, 9 | publisher={American Society of Mechanical Engineers} 10 | } -------------------------------------------------------------------------------- /citations/bezier-gan.txt: -------------------------------------------------------------------------------- 1 | @article{chen2020airfoil, 2 | title={Airfoil design parameterization and optimization using b{\'e}zier generative adversarial networks}, 3 | author={Chen, Wei and Chiu, Kevin and Fuge, Mark D}, 4 | journal={AIAA journal}, 5 | volume={58}, 6 | number={11}, 7 | pages={4723--4735}, 8 | year={2020}, 9 | publisher={American Institute of Aeronautics and Astronautics} 10 | } -------------------------------------------------------------------------------- /citations/bspline-gan.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{Du2020, 2 | address = {Orlando, FL}, 3 | author = {Du, Xiaosong and He, Ping and Martins, Joaquim R. R. A.}, 4 | booktitle = {AIAA SciTech Forum}, 5 | doi = {10.2514/6.2020-2128}, 6 | keywords = {ank}, 7 | month = jan, 8 | organization = {AIAA}, 9 | title = {A {B}-Spline-based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization}, 10 | year = {2020} 11 | } -------------------------------------------------------------------------------- /citations/extensible.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{ 2 | bonnet2022an, 3 | title={An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations}, 4 | author={Florent Bonnet and Jocelyn Ahmed Mazari and Thibaut Munzer and Pierre Yser and Patrick Gallinari}, 5 | booktitle={ICLR 2022 Workshop on Geometrical and Topological Representation Learning}, 6 | year={2022}, 7 | url={https://openreview.net/forum?id=rqUUi4-kpeq} 8 | } -------------------------------------------------------------------------------- /citations/g2aero.txt: -------------------------------------------------------------------------------- 1 | @article{GreyJCDE2023, 2 | author = {Grey, Zachary J and Doronina, Olga A and Glaws, Andrew}, 3 | title = "{Separable shape tensors for aerodynamic design}", 4 | journal = {Journal of Computational Design and Engineering}, 5 | volume = {10}, 6 | number = {1}, 7 | pages = {468-487}, 8 | year = {2023}, 9 | month = {01}, 10 | doi = {10.1093/jcde/qwac140}, 11 | url = {https://doi.org/10.1093/jcde/qwac140}, 12 | } -------------------------------------------------------------------------------- /citations/TransCAD.txt: -------------------------------------------------------------------------------- 1 | @misc{dupont2024transcadhierarchicaltransformercad, 2 | title={TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds}, 3 | author={Elona Dupont and Kseniya Cherenkova and Dimitrios Mallis and Gleb Gusev and Anis Kacem and Djamila Aouada}, 4 | year={2024}, 5 | eprint={2407.12702}, 6 | archivePrefix={arXiv}, 7 | primaryClass={cs.CV}, 8 | url={https://arxiv.org/abs/2407.12702}, 9 | } -------------------------------------------------------------------------------- /citations/SolidGen.txt: -------------------------------------------------------------------------------- 1 | @misc{jayaraman2023solidgenautoregressivemodeldirect, 2 | title={SolidGen: An Autoregressive Model for Direct B-rep Synthesis}, 3 | author={Pradeep Kumar Jayaraman and Joseph G. Lambourne and Nishkrit Desai and Karl D. D. Willis and Aditya Sanghi and Nigel J. W. Morris}, 4 | year={2023}, 5 | eprint={2203.13944}, 6 | archivePrefix={arXiv}, 7 | primaryClass={cs.LG}, 8 | url={https://arxiv.org/abs/2203.13944}, 9 | } -------------------------------------------------------------------------------- /citations/airfRANS.txt: -------------------------------------------------------------------------------- 1 | @inproceedings{ 2 | bonnet2022airfrans, 3 | title={Airf{RANS}: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier{\textendash}Stokes Solutions}, 4 | author={Florent Bonnet and Jocelyn Ahmed Mazari and Paola Cinnella and Patrick Gallinari}, 5 | booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, 6 | year={2022}, 7 | url={https://arxiv.org/abs/2212.07564} 8 | } -------------------------------------------------------------------------------- /citations/text2cad.txt: -------------------------------------------------------------------------------- 1 | @misc{khan2024text2cadgeneratingsequentialcad, 2 | title={Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts}, 3 | author={Mohammad Sadil Khan and Sankalp Sinha and Talha Uddin Sheikh and Didier Stricker and Sk Aziz Ali and Muhammad Zeshan Afzal}, 4 | year={2024}, 5 | eprint={2409.17106}, 6 | archivePrefix={arXiv}, 7 | primaryClass={cs.CV}, 8 | url={https://arxiv.org/abs/2409.17106}, 9 | } -------------------------------------------------------------------------------- /citations/afbench.txt: -------------------------------------------------------------------------------- 1 | @misc{liu2024afbenchlargescalebenchmarkairfoil, 2 | title={AFBench: A Large-scale Benchmark for Airfoil Design}, 3 | author={Jian Liu and Jianyu Wu and Hairun Xie and Guoqing Zhang and Jing Wang and Wei Liu and Wanli Ouyang and Junjun Jiang and Xianming Liu and Shixiang Tang and Miao Zhang}, 4 | year={2024}, 5 | eprint={2406.18846}, 6 | archivePrefix={arXiv}, 7 | primaryClass={cs.CE}, 8 | url={https://arxiv.org/abs/2406.18846}, 9 | } -------------------------------------------------------------------------------- /citations/superlative_me.txt: -------------------------------------------------------------------------------- 1 | @article{Snapp2024SuperlativeME, 2 | title={Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership}, 3 | author={Kelsey L. Snapp and Benjamin Verdier and Aldair E. Gongora and Samuel Silverman and Adedire D. Adesiji and Elise F Morgan and Timothy J. Lawton and Emily Whiting and Keith A Brown}, 4 | journal={Nature Communications}, 5 | year={2024}, 6 | volume={15}, 7 | url={https://api.semanticscholar.org/CorpusID:269948540} 8 | } -------------------------------------------------------------------------------- /citations/cst-gan.txt: -------------------------------------------------------------------------------- 1 | @INPROCEEDINGS{9987080, 2 | author={Lin, Jinxing and Zhang, Chenliang and Xie, Xiaoye and Shi, Xingyu and Xu, Xiaoyu and Duan, Yanhui}, 3 | booktitle={2022 IEEE International Conference on Unmanned Systems (ICUS)}, 4 | title={CST-GANs: A Generative Adversarial Network Based on CST Parameterization for the Generation of Smooth Airfoils}, 5 | year={2022}, 6 | volume={}, 7 | number={}, 8 | pages={600-605}, 9 | keywords={Geometry;Shape;Atmospheric modeling;Neural networks;Fitting;Optimization methods;Aerodynamics;Generative Adversarial Networks;CST;ASO}, 10 | doi={10.1109/ICUS55513.2022.9987080}} 11 | -------------------------------------------------------------------------------- /citations/creativegan.txt: -------------------------------------------------------------------------------- 1 | @proceedings{10.1115/DETC2021-68103, 2 |     author = {Heyrani Nobari, 3 |     Amin and Rashad, 4 |     Muhammad Fathy and Ahmed, 5 |     Faez}, 6 |     title = {CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis}, 7 |    volume = {Volume 3A: 47th Design Automation Conference (DAC)}, 8 |     series = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference}, 9 |     year = {2021}, 10 |     month = {08}, 11 |     doi = {10.1115/DETC2021-68103}, 12 |     url = {https://doi.org/10.1115/DETC2021-68103}, 13 |     note = {V03AT03A002}, 14 | } -------------------------------------------------------------------------------- /citations/airfoil-cvae-lift.txt: -------------------------------------------------------------------------------- 1 | @article{10.1016/j.engappai.2021.104560, 2 | author = {Yonekura, Kazuo and Wada, Kazunari and Suzuki, Katsuyuki}, 3 | title = {Generating various airfoils with required lift coefficients by combining NACA and Joukowski airfoils using conditional variational autoencoders}, 4 | year = {2022}, 5 | issue_date = {Feb 2022}, 6 | publisher = {Pergamon Press, Inc.}, 7 | address = {USA}, 8 | volume = {108}, 9 | number = {C}, 10 | issn = {0952-1976}, 11 | url = {https://doi.org/10.1016/j.engappai.2021.104560}, 12 | doi = {10.1016/j.engappai.2021.104560}, 13 | journal = {Eng. Appl. Artif. Intell.}, 14 | month = {feb}, 15 | numpages = {13}, 16 | keywords = {Airfoil design, Variational autoencoder, Inverse problem, Design exploration} 17 | } -------------------------------------------------------------------------------- /citations/airfoil-pressure.txt: -------------------------------------------------------------------------------- 1 | @ARTICLE{9547003, 2 | author={Wang, Yueqing and Deng, Liang and Wan, Yunbo and Yang, Zhigong and Yang, Wenxiang and Chen, Cheng and Zhao, Dan and Wang, Fang and Guo, Yang}, 3 | journal={IEEE Transactions on Neural Networks and Learning Systems}, 4 | title={An Intelligent Method for Predicting the Pressure Coefficient Curve of Airfoil-Based Conditional Generative Adversarial Networks}, 5 | year={2023}, 6 | volume={34}, 7 | number={7}, 8 | pages={3538-3552}, 9 | keywords={Automotive components;Aerodynamics;Shape;Computational modeling;Atmospheric modeling;Generative adversarial networks;Aircraft;Aerodynamic coefficient;airfoil;conditional generative adversarial network (cGAN);info-generative adversarial network (GAN);pressure coefficient}, 10 | doi={10.1109/TNNLS.2021.3111911} 11 | } -------------------------------------------------------------------------------- /citations/improved_CcGAN.txt: -------------------------------------------------------------------------------- 1 | @ARTICLE{9983478, 2 | author={Ding, Xin and Wang, Yongwei and Xu, Zuheng and Welch, William J. and Wang, Z. Jane}, 3 | journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 4 | title={Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms}, 5 | year={2023}, 6 | volume={45}, 7 | number={7}, 8 | pages={8143-8158}, 9 | doi={10.1109/TPAMI.2022.3228915} 10 | } 11 | 12 | @inproceedings{ 13 | ding2021ccgan, 14 | title={Cc{GAN}: Continuous Conditional Generative Adversarial Networks for Image Generation}, 15 | author={Xin Ding and Yongwei Wang and Zuheng Xu and William J Welch and Z. Jane Wang}, 16 | booktitle={International Conference on Learning Representations}, 17 | year={2021}, 18 | url={https://openreview.net/forum?id=PrzjugOsDeE} 19 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Jian Liu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /citations/airfoil-cvae.txt: -------------------------------------------------------------------------------- 1 | @article{10.1007/s00158-021-02851-0, 2 | author = {Yonekura, Kazuo and Suzuki, Katsuyuki}, 3 | title = {Data-driven design exploration method using conditional variational autoencoder for airfoil design}, 4 | year = {2021}, 5 | issue_date = {Aug 2021}, 6 | publisher = {Springer-Verlag}, 7 | address = {Berlin, Heidelberg}, 8 | volume = {64}, 9 | number = {2}, 10 | issn = {1615-147X}, 11 | url = {https://doi.org/10.1007/s00158-021-02851-0}, 12 | doi = {10.1007/s00158-021-02851-0}, 13 | abstract = {An objective of mechanical design is to obtain a shape that satisfies specific requirements. In the present work, we achieve this goal using a conditional variational autoencoder (CVAE). The method enables us to analyze the relationship between aerodynamic performance and the shape of aerodynamic parts, and to explore new designs for the parts. In the CVAE model, a shape is fed as an input and the corresponding aerodynamic performance index is fed as a continuous label. Then, shapes are generated by specifying the continuous label and latent vector. When CVAE is applied to mechanical design, it is desired to draw shapes that reproduce the specified aerodynamic performance. In ordinal CVAE, the model is trained to minimize reconstruction loss and latent loss, and it is usually optimized considering the sum of these losses. However, the present study shows that the optimal network is not always optimal in terms of reproducing the aerodynamic performance. The proposed method is verified using two numerical examples: a two-dimensional (2D) airfoil and a turbine blade. In the airfoil example, we demonstrate the effects of latent dimension, and in the turbine design example, we demonstrate that the proposed method can be applied to a real turbine design problem and reduce the design time.}, 14 | journal = {Struct. Multidiscip. Optim.}, 15 | month = {aug}, 16 | pages = {613–624}, 17 | numpages = {12}, 18 | keywords = {Airfoil design, Variational autoencoder, Design exploration} 19 | } 20 | -------------------------------------------------------------------------------- /citations/airfoil-cgan-gp.txt: -------------------------------------------------------------------------------- 1 | @article{10.1115/1.4052846, 2 | author = {Chen, Qiuyi and Wang, Jun and Pope, Phillip and (Wayne) Chen, Wei and Fuge, Mark}, 3 | title = "{Inverse Design of Two-Dimensional Airfoils Using Conditional Generative Models and Surrogate Log-Likelihoods}", 4 | journal = {Journal of Mechanical Design}, 5 | volume = {144}, 6 | number = {2}, 7 | pages = {021712}, 8 | year = {2021}, 9 | month = {12}, 10 | abstract = "{This paper shows how to use conditional generative models in two-dimensional (2D) airfoil optimization to probabilistically predict good initialization points within the vicinity of the optima given the input boundary conditions, thus warm starting and accelerating further optimization. We accommodate the possibility of multiple optimal designs corresponding to the same input boundary condition and take this inversion ambiguity into account when designing our prediction framework. To this end, we first employ the conditional formulation of our previous work BézierGAN–Conditional BézierGAN (CBGAN)—as a baseline, then introduce its sibling conditional entropic BézierGAN (CEBGAN), which is based on optimal transport regularized with entropy. Compared with CBGAN, CEBGAN overcomes mode collapse plaguing conventional GANs, improves the average lift-drag (Cl/Cd) efficiency of airfoil predictions from 80.8\\% of the optimal value to 95.8\\%, and meanwhile accelerates the training process by 30.7\\%. Furthermore, we investigate the unique ability of CEBGAN to produce a log-likelihood lower bound that may help select generated samples of higher performance (e.g., aerodynamic performance). In addition, we provide insights into the performance differences between these two models with low-dimensional toy problems and visualizations. These results and the probabilistic formulation of this inverse problem justify the extension of our GAN-based inverse design paradigm to other inverse design problems or broader inverse problems.}", 11 | issn = {1050-0472}, 12 | doi = {10.1115/1.4052846}, 13 | url = {https://doi.org/10.1115/1.4052846}, 14 | eprint = {https://asmedigitalcollection.asme.org/mechanicaldesign/article-pdf/144/2/021712/6806632/md\_144\_2\_021712.pdf}, 15 | } 16 | -------------------------------------------------------------------------------- /citations/airfoil-geo.txt: -------------------------------------------------------------------------------- 1 | @article{XIE2024107505, 2 | title = {Parametric generative schemes with geometric constraints for encoding and synthesizing airfoils}, 3 | journal = {Engineering Applications of Artificial Intelligence}, 4 | volume = {128}, 5 | pages = {107505}, 6 | year = {2024}, 7 | issn = {0952-1976}, 8 | doi = {https://doi.org/10.1016/j.engappai.2023.107505}, 9 | url = {https://www.sciencedirect.com/science/article/pii/S0952197623016895}, 10 | author = {Hairun Xie and Jing Wang and Miao Zhang}, 11 | keywords = {Deep learning, Parameterization method, Parametric generative schemes, Parametric airfoil, Geometric constraints}, 12 | abstract = {The modern aerodynamic optimization has a strong demand for parametric methods with high levels of intuitiveness, flexibility, and representative accuracy, which cannot be fully achieved through traditional airfoil parametric techniques. In this paper, two deep learning-based generative schemes are proposed to effectively capture the complexity of the design space while satisfying specific constraints. 1. Soft-constrained scheme: a Conditional Variational Autoencoder-based model that directly incorporates geometric constraints as part of the network. 2. Hard-constrained scheme: a Variational Autoencoder-based model for generating diverse airfoils, with projection onto given constraints using Free Form Deformation-based techniques. According to the statistical results, the reconstructed airfoils are both accurate and smooth, without any need for additional filters. The soft-constrained scheme generates airfoils that exhibit slight deviations from the expected geometric constraints, yet still converge to the reference airfoil in both geometry space and objective space with some degree of distribution bias. In contrast, the hard-constrained scheme produces airfoils with a wider range of geometric diversity while strictly adhering to the geometric constraints. The corresponding distribution in the objective space is also more diverse, with isotropic uniformity around the reference point and no significant bias. These proposed airfoil parametric methods can break through the boundaries of training data in the objective space, providing higher quality samples for random sampling and improving the efficiency of optimization design.} 13 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-Engineer-Design [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 2 | A curated list of awesome engineer design papers, inspired by [awesome-aigc-3d](https://github.com/hitcslj/Awesome-AIGC-3D). 3 | 4 | 5 | 6 | 7 | 8 | 9 | #### [How to submit a pull request?](https://github.com/hitcslj/Awesome-Engineer-Design/blob/main/how-to-PR.md) 10 | 11 | 12 | 13 | ## Table of Contents 14 | 15 | - [Awesome-Engineer-Design ](#awesome-engineer-design-) 16 | - [How to submit a pull request?](#how-to-submit-a-pull-request) 17 | - [Table of Contents](#table-of-contents) 18 | - [Survey](#survey) 19 | - [Papers](#papers) 20 | - [Benchmarks and Datasets](#benchmarks-and-datasets) 21 | - [Challenges](#Challenges) 22 | - [Talks](#talks) 23 | - [Company\&Team\&Experts](#companyteamexperts) 24 | - [Implementations](#implementations) 25 | - [Notes](#notes) 26 | - [License](#license) 27 | - [Citation](#citation) 28 | - [Contact](#contact) 29 | 30 | ## Survey 31 | 32 | - [Deep Generative Models in Engineering Design: A Review](https://arxiv.org/abs/2110.10863), Regenwetter et al., JMD 2022 | [bibtex](./citations/deepED.txt) 33 | - [Machine Learning in Aerodynamic Shape Optimization](https://arxiv.org/abs/2202.07141), Li et al., Prog. Aerosp. Sci 2022 | [bibtex](./citations/ml-aso.txt) 34 | 35 | ## Papers 36 | 37 | 38 |
39 | Airfoil Inverse Design 40 | 41 | - [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) 42 | - [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) 43 | - [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) 44 | - [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) 45 | - [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) 46 | - [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 47 | 2021 | [bibtex](./citations/cvae-gan.txt) 48 | - [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) 49 | - [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) 50 | - [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) 51 | - [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) 52 | - [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) 53 | - [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) 54 | - [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) 55 | - [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) 56 | 57 |
58 | 59 | 60 |
61 | Airfoil Parameterization & Shape Optimization 62 | 63 | - [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) 64 | - [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) 65 | - [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) 66 | - [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) 67 | - [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) 68 | - [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) 69 | - [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) 70 | - [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) 71 | - [Airfoil Optimization using Design-by-Morphing](https://arxiv.org/abs/2207.11448), Sheikh et al., JCDE 2023 | [bibtex](./citations/airfoil-morph.txt) 72 | - [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) 73 | - [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) 74 | - [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) 75 | 76 | 77 |
78 | 79 | 80 |
81 | Airfoil Editing 82 | 83 | > TODO 84 | 85 |
86 | 87 |
88 | Airfoil aerodynamic performace prediction 89 | 90 | > 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). 91 | 92 | 93 | - [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) 94 | - [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) 95 | - [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) 96 | - [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) 97 | - [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) 98 | - [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) 99 | - [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) 100 | 101 |
102 | 103 | 104 |
105 | CAD Design 106 | 107 | - [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) 108 | - [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) 109 | - [SolidGen: An Autoregressive Model for Direct B-rep Synthesis](https://arxiv.org/abs/2203.13944), Jayaraman etal., TMLR 2023 | [bibtex](./citations/SolidGen.txt) 110 | - [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) 111 | 112 |
113 | 114 | 115 | 116 |
117 | Other engineer design 118 | 119 | - [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) 120 | - [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) 121 | - [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) 122 | - [Using Graph Neural Networks for Additive Manufacturing](https://developer.nvidia.com/blog/using-graph-neural-networks-for-additive-manufacturing/), Jain et al., NVIDIA 123 | - [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) 124 | - [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 125 | - [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) 126 | 127 | 128 |
129 | 130 | 131 | 132 | 133 | 134 | ## Benchmarks and Datasets 135 | 136 | - [UIUC Airfoil data](https://m-selig.ae.illinois.edu/ads_barton.html) 137 | - [BigFoil](https://www.bigfoil.com/) 138 | - [G2Aero](https://arxiv.org/abs/2208.04743), Grey et al., JCDE 2023 | [github](https://github.com/NREL/G2Aero) | [bibtex](./citations/g2aero.txt) 139 | - [AFBench](https://arxiv.org/abs/2406.18846), Liu et al., NeurIPS 2024 | [github](https://github.com/open-sciencelab/Intern-WingWing) | [bibtex](./citations/afbench.txt) 140 | 141 | ## Challenges 142 | 143 | - [NeurIPS2024-ML4CFD-competition](https://github.com/IRT-SystemX/NeurIPS2024-ML4CFD-competition-Starting-Kit) 144 | 145 | 146 | ## Talks 147 | > TODO 148 | 149 | 150 | ## Company&Team&Experts 151 | 152 | - [Design Computation and Digital Engineering (DeCoDE) Lab](https://decode.mit.edu/), MIT | 153 | - [Design, Engineering And Learning (IDEAL) Lab](https://ideal.umd.edu/), UMD | [github](https://github.com/IDEALLAB) 154 | - [Wei Chen](https://scholar.google.com/citations?hl=en&user=UlTyOWMAAAAJ&view_op=list_works&sortby=pubdate), UMD 155 | - [Extrality](https://github.com/Extrality) 156 | - [AutoDesk](https://www.autodesk.com/design-make/emerging-tech/generative-design) 157 | - [Zoo: Building Infrastructure for Hardware Designers](https://github.com/kittycad) 158 | 159 | ## Implementations 160 | 161 | - [XFoil](https://web.mit.edu/drela/Public/web/xfoil/), MIT 162 | - [AeroSandbox](https://github.com/peterdsharpe/AeroSandbox), Peter D. | [bibtex](./citations/aerosandbox.txt) 163 | - [adflow](https://github.com/mdolab/adflow), Mader et al., JAIS 2020 | [bibtex](./citations/adflow.txt) 164 | - [airfoil-interpolation](https://github.com/IDEALLab/airfoil-interpolation), Chen 165 | - [Anton: generative design framework](https://github.com/blender-for-science/anton) 166 | - [text-to-CAD](https://zoo.dev/text-to-cad), Zoo et al., | [github](https://github.com/KittyCAD/text-to-cad-ui) 167 | 168 | ## Notes 169 | 170 | - [physics-based deep learning](https://physicsbaseddeeplearning.org/intro.html), Thuerey et al., WWW 2021 | [bibtex](./citations/pbdl.txt) 171 | - [Autodesk’s AI Innovations Transforming Sustainable Design and Construction](https://www.research.autodesk.com/blog/autodesks-ai-innovations-transforming-sustainable-design-and-construction/), Autodesk 172 | 173 | ## License 174 | Awesome Engineer Design is released under the [MIT license](./LICENSE). 175 | 176 | ## Citation 177 | > TODO 178 | 179 | ## Contact 180 | contact: `hitcslj@stu.hit.edu.cn`. 181 | --------------------------------------------------------------------------------