IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i22p9841-d1518870.html
   My bibliography  Save this article

The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning

Author

Listed:
  • Zhenchong Mo

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Lin Gong

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314000, China)

  • Mingren Zhu

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Junde Lan

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Large language model (LLM) and Crowd Intelligent Innovation (CII) are reshaping the field of engineering design and becoming a new design context. Generative generic-field design can solve more general design problems innovatively by integrating multi-domain design knowledge. However, there is a lack of knowledge representation and design process model in line with the design cognition of the new context. It is urgent to develop generative generic-field design methods to improve the feasibility, innovation, and empathy of design results. This study proposes a method based on design cognition and knowledge reasoning. Firstly, through the problem formulation, a generative universal domain design framework and knowledge base are constructed. Secondly, the knowledge-based discrete physical structure set generation method and system architecture generation method are proposed. Finally, the application tool Intelligent Design Assistant (IDA) is developed, verified, and discussed through an engineering design case. According to the design results and discussion, the design scheme is feasible and reflects empathy for the fuzzy original design requirements. Therefore, the method proposed in this paper is an effective technical scheme of generative generic-field engineering design in line with the design cognition in the new context.

Suggested Citation

  • Zhenchong Mo & Lin Gong & Mingren Zhu & Junde Lan, 2024. "The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning," Sustainability, MDPI, vol. 16(22), pages 1-34, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9841-:d:1518870
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/22/9841/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/22/9841/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    2. Frédéric Lavancier & Jesper Møller & Ege Rubak, 2015. "Determinantal point process models and statistical inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 853-877, September.
    3. Julian Schrittwieser & Ioannis Antonoglou & Thomas Hubert & Karen Simonyan & Laurent Sifre & Simon Schmitt & Arthur Guez & Edward Lockhart & Demis Hassabis & Thore Graepel & Timothy Lillicrap & David , 2020. "Mastering Atari, Go, chess and shogi by planning with a learned model," Nature, Nature, vol. 588(7839), pages 604-609, December.
    4. Qiao Wen & Rui-Guang Zhu, 2020. "Automatic Generation of 3D Building Models Based on Line Segment Vectorization," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-16, October.
    5. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    6. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    7. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    2. Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    3. Patrick Bryant & Gabriele Pozzati & Wensi Zhu & Aditi Shenoy & Petras Kundrotas & Arne Elofsson, 2022. "Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    5. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    6. Sun-Ting Tsai & Eric Fields & Yijia Xu & En-Jui Kuo & Pratyush Tiwary, 2022. "Path sampling of recurrent neural networks by incorporating known physics," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Wanyang Dai, 2024. "Stochastic Differential Games and a Unified Forward–Backward Coupled Stochastic Partial Differential Equation with Lévy Jumps," Mathematics, MDPI, vol. 12(18), pages 1-46, September.
    8. Minkyu Shin & Jin Kim & Bas van Opheusden & Thomas L. Griffiths, 2023. "Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty," Papers 2303.07462, arXiv.org, revised Apr 2023.
    9. De Moor, Bram J. & Gijsbrechts, Joren & Boute, Robert N., 2022. "Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management," European Journal of Operational Research, Elsevier, vol. 301(2), pages 535-545.
    10. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
    11. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    12. Xiaoxuan Pan & Zhide Lu & Weiting Wang & Ziyue Hua & Yifang Xu & Weikang Li & Weizhou Cai & Xuegang Li & Haiyan Wang & Yi-Pu Song & Chang-Ling Zou & Dong-Ling Deng & Luyan Sun, 2023. "Deep quantum neural networks on a superconducting processor," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    13. Ye Yuan & Lei Chen & Kexu Song & Miaomiao Cheng & Ling Fang & Lingfei Kong & Lanlan Yu & Ruonan Wang & Zhendong Fu & Minmin Sun & Qian Wang & Chengjun Cui & Haojue Wang & Jiuyang He & Xiaonan Wang & Y, 2024. "Stable peptide-assembled nanozyme mimicking dual antifungal actions," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    14. Ivica Odorčić & Mohamed Belal Hamed & Sam Lismont & Lucía Chávez-Gutiérrez & Rouslan G. Efremov, 2024. "Apo and Aβ46-bound γ-secretase structures provide insights into amyloid-β processing by the APH-1B isoform," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. Tian Zhu & Merry H. Ma, 2022. "Deriving the Optimal Strategy for the Two Dice Pig Game via Reinforcement Learning," Stats, MDPI, vol. 5(3), pages 1-14, August.
    16. Stella Vitt & Simone Prinz & Martin Eisinger & Ulrich Ermler & Wolfgang Buckel, 2022. "Purification and structural characterization of the Na+-translocating ferredoxin: NAD+ reductase (Rnf) complex of Clostridium tetanomorphum," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    17. Pierre Azoulay & Joshua Krieger & Abhishek Nagaraj, 2024. "Old Moats for New Models: Openness, Control, and Competition in Generative AI," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, National Bureau of Economic Research, Inc.
    18. Riya Shah & Thomas C. Panagiotou & Gregory B. Cole & Trevor F. Moraes & Brigitte D. Lavoie & Christopher A. McCulloch & Andrew Wilde, 2024. "The DIAPH3 linker specifies a β-actin network that maintains RhoA and Myosin-II at the cytokinetic furrow," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    19. Yashan Yang & Qianqian Shao & Mingcheng Guo & Lin Han & Xinyue Zhao & Aohan Wang & Xiangyun Li & Bo Wang & Ji-An Pan & Zhenguo Chen & Andrei Fokine & Lei Sun & Qianglin Fang, 2024. "Capsid structure of bacteriophage ΦKZ provides insights into assembly and stabilization of jumbo phages," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    20. Bret M. Boyd & Ian James & Kevin P. Johnson & Robert B. Weiss & Sarah E. Bush & Dale H. Clayton & Colin Dale, 2024. "Stochasticity, determinism, and contingency shape genome evolution of endosymbiotic bacteria," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9841-:d:1518870. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.