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Hybrid Twin: An Intimate Alliance of Knowledge and Data

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The Digital Twin

Abstract

Models based on physics were the major protagonists of the Simulation Based Engineering Sciences during the last century. However, engineering is focusing the more and more on performances. Thus, the new engineering must conciliate two usually opposite requirements: fast and accurate. With the irruption of data, and the technologies for efficiently manipulating it, in particular artificial intelligence and machine learning, data serves to enrich physics-based models, and the last allows data becoming smarter. When combined, physics-based and data-driven models, within the concept of Hybrid Twin, real-time predictions are possible while ensuring the highest accuracy. This chapter introduces the Hybrid Twin concept, with the associated technologies, applications and business model.

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References

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Author information

Authors and Affiliations

  1. Arts et Métiers Institute of Technology, Paris, France

    Francisco Chinesta

  2. ESI Group, Paris, France

    Francisco Chinesta & Fouad El Khaldi

  3. I3A, Universidad de Zaragoza, Zaragoza, Spain

    Elias Cueto

Authors
  1. Francisco Chinesta
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  2. Fouad El Khaldi
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  3. Elias Cueto
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Corresponding author

Correspondence to Francisco Chinesta .

Editor information

Editors and Affiliations

  1. Telecom SudParis, Institut Polytechnique de Paris, Palaiseau, France

    Noel Crespi

  2. OpenTechWorks Inc., Wayne, PA, USA

    Adam T. Drobot

  3. Telecom SudParis, Institut Polytechnique de Paris, Palaiseau, France

    Roberto Minerva

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Chinesta, F., El Khaldi, F., Cueto, E. (2023). Hybrid Twin: An Intimate Alliance of Knowledge and Data. In: Crespi, N., Drobot, A.T., Minerva, R. (eds) The Digital Twin. Springer, Cham. https://doi.org/10.1007/978-3-031-21343-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-21343-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21342-7

  • Online ISBN: 978-3-031-21343-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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