IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i12p2661-d1168627.html
   My bibliography  Save this article

Digital Triplet: A Sequential Methodology for Digital Twin Learning

Author

Listed:
  • Xueru Zhang

    (Department of Statistics, Purdue University, West Lafayette, IN 47907, USA)

  • Dennis K. J. Lin

    (Department of Statistics, Purdue University, West Lafayette, IN 47907, USA)

  • Lin Wang

    (Department of Statistics, Purdue University, West Lafayette, IN 47907, USA)

Abstract

A digital twin is a simulator of a physical system, which is built upon a series of models and computer programs with real-time data (from sensors or devices). Digital twins are used in various industries, such as manufacturing, healthcare, and transportation, to understand complex physical systems and make informed decisions. However, predictions and optimizations with digital twins can be time-consuming due to the high computational requirements and complexity of the underlying computer programs. This poses significant challenges in making well-informed and timely decisions using digital twins. This paper proposes a novel methodology, called the “digital triplet”, to facilitate real-time prediction and decision-making. A digital triplet is an efficient representation of a digital twin, constructed using statistical models and effective experimental designs. It offers two noteworthy advantages. Firstly, by leveraging modern statistical models, a digital triplet can effectively capture and represent the complexities of a digital twin, resulting in accurate predictions and reliable decision-making. Secondly, a digital triplet adopts a sequential design and modeling approach, allowing real-time updates in conjunction with its corresponding digital twin. We conduct comprehensive simulation studies to explore the application of various statistical models and designs in constructing a digital triplet. It is shown that Gaussian process regression coupled with sequential MaxPro designs exhibits superior performance compared to other modeling and design techniques in accurately constructing the digital triplet.

Suggested Citation

  • Xueru Zhang & Dennis K. J. Lin & Lin Wang, 2023. "Digital Triplet: A Sequential Methodology for Digital Twin Learning," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2661-:d:1168627
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/12/2661/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/12/2661/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
    2. V. Roshan Joseph & Evren Gul & Shan Ba, 2015. "Maximum projection designs for computer experiments," Biometrika, Biometrika Trust, vol. 102(2), pages 371-380.
    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. Jian-Guo Duan & Tian-Yu Ma & Qing-Lei Zhang & Zhen Liu & Ji-Yun Qin, 2023. "Design and application of digital twin system for the blade-rotor test rig," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 753-769, February.
    2. Leurent, Martin & Jasserand, Frédéric & Locatelli, Giorgio & Palm, Jenny & Rämä, Miika & Trianni, Andrea, 2017. "Driving forces and obstacles to nuclear cogeneration in Europe: Lessons learnt from Finland," Energy Policy, Elsevier, vol. 107(C), pages 138-150.
    3. Francisco Castillo-Zunino & Pinar Keskinocak, 2021. "Bi-criteria multiple knapsack problem with grouped items," Journal of Heuristics, Springer, vol. 27(5), pages 747-789, October.
    4. Xinzhou Wu & Zhe Cheng & Victor E. Kuzmichev, 2023. "Dynamic Fit Optimization and Effect Evaluation of a Female Wetsuit Based on Virtual Technology," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    5. Song-Nan Liu & Min-Qian Liu & Jin-Yu Yang, 2023. "Construction of Column-Orthogonal Designs with Two-Dimensional Stratifications," Mathematics, MDPI, vol. 11(6), pages 1-27, March.
    6. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    7. Hongjun Li & Yu Yang & Chi Zhang & Chengjun Zhang & Wei Chen, 2023. "Visualization Monitoring of Industrial Detonator Automatic Assembly Line Based on Digital Twin," Sustainability, MDPI, vol. 15(9), pages 1-16, May.
    8. Evangelos Katsamakas, 2024. "Business models for the simulation hypothesis," Papers 2404.08991, arXiv.org.
    9. Yue Huan & Yubin Tian & Dianpeng Wang, 2022. "A Weighted Surrogate Model for Spatio-Temporal Dynamics with Multiple Time Spans: Applications for the Pollutant Concentration of the Bai River," Mathematics, MDPI, vol. 10(19), pages 1-16, October.
    10. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
    11. Mu, Weiyan & Xiong, Shifeng, 2018. "A class of space-filling designs and their projection properties," Statistics & Probability Letters, Elsevier, vol. 141(C), pages 129-134.
    12. Milena Kajba & Borut Jereb & Tina Cvahte Ojsteršek, 2023. "Exploring Digital Twins in the Transport and Energy Fields: A Bibliometrics and Literature Review Approach," Energies, MDPI, vol. 16(9), pages 1-23, May.
    13. Bai, Fan & Quan, Hong-Bing & Yin, Ren-Jie & Zhang, Zhuo & Jin, Shu-Qi & He, Pu & Mu, Yu-Tong & Gong, Xiao-Ming & Tao, Wen-Quan, 2022. "Three-dimensional multi-field digital twin technology for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 324(C).
    14. Jaljolie, Ruba & Riekkinen, Kirsikka & Dalyot, Sagi, 2021. "A topological-based approach for determining spatial relationships of complex volumetric parcels in land administration systems," Land Use Policy, Elsevier, vol. 109(C).
    15. Muhammad Ali Musarat & Alishba Sadiq & Wesam Salah Alaloul & Mohamed Mubarak Abdul Wahab, 2022. "A Systematic Review on Enhancement in Quality of Life through Digitalization in the Construction Industry," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    16. Chao Ke & Xiuyan Pan & Pan Wan & Zixi Huang & Zhigang Jiang, 2023. "An Intelligent Redesign Method for Used Products Based on Digital Twin," Sustainability, MDPI, vol. 15(12), pages 1-19, June.
    17. Guillaume Perrin & Christian Soize, 2020. "Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework," Computational Statistics, Springer, vol. 35(1), pages 111-133, March.
    18. Dianyou Yu & Zheng He, 2022. "Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 1-36, May.
    19. Farzam Farbiz & Mohd Salahuddin Habibullah & Brahim Hamadicharef & Tomasz Maszczyk & Saurabh Aggarwal, 2023. "Knowledge-embedded machine learning and its applications in smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2889-2906, October.
    20. Junli Liu & Deyu Zhang & Zhongpeng Liu & Tianyu Guo & Yanyan Yan, 2024. "Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout," Sustainability, MDPI, vol. 16(20), pages 1-22, October.

    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:jmathe:v:11:y:2023:i:12:p:2661-:d:1168627. 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.