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2023 | OriginalPaper | Buchkapitel

Hybrid Twin: An Intimate Alliance of Knowledge and Data

verfasst von : Francisco Chinesta, Fouad El Khaldi, Elias Cueto

Erschienen in: The Digital Twin

Verlag: Springer International Publishing

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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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Literatur
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Zurück zum Zitat Yun, M., Argerich, C., Gilormini, P., Chinesta, F., Advani, S. (2020). Predicting data-driven fiber-fiber interactions in semi-concentrated flowing suspensions. Entropy, 22(30). Yun, M., Argerich, C., Gilormini, P., Chinesta, F., Advani, S. (2020). Predicting data-driven fiber-fiber interactions in semi-concentrated flowing suspensions. Entropy, 22(30).
18.
Zurück zum Zitat Frahi, T., Chinesta, F., Falco, A., Badias, A., Cueto, E., Choi, H. Y., Han, M., & Duval, J.-L. (2021). Empowering advanced driver-assistance systems from topological data analysis. Mathematics, 9(6), 634.CrossRef Frahi, T., Chinesta, F., Falco, A., Badias, A., Cueto, E., Choi, H. Y., Han, M., & Duval, J.-L. (2021). Empowering advanced driver-assistance systems from topological data analysis. Mathematics, 9(6), 634.CrossRef
19.
Zurück zum Zitat Moya, B., Badías, A., Alfaro, I., Chinesta, F., & Cueto, E. (2020). Digital twins that learn and correct themselves. International Journal for Numerical Methods in Engineering. Accepted for publication. Moya, B., Badías, A., Alfaro, I., Chinesta, F., & Cueto, E. (2020). Digital twins that learn and correct themselves. International Journal for Numerical Methods in Engineering. Accepted for publication.
20.
Zurück zum Zitat Ibañez, R., Abisset-Chavanne, E., Ammar, A., González, D., Cueto, E., Huerta, A., Duval, J. L., & Chinesta, F. (2018). A multi-dimensional data-driven sparse identification technique: the sparse Proper Generalized Decomposition. Complexity. Paper 5608286. Ibañez, R., Abisset-Chavanne, E., Ammar, A., González, D., Cueto, E., Huerta, A., Duval, J. L., & Chinesta, F. (2018). A multi-dimensional data-driven sparse identification technique: the sparse Proper Generalized Decomposition. Complexity. Paper 5608286.
21.
Zurück zum Zitat Sancarlos, A., Cameron, M., Le Peuvedic, J.-M., Groulier, J., Duval, J.-L., Cueto, E., & Chinesta, F. (2021). Learning stable reduced-order models for hybrid twins. Data-Centric Engineering. Data-Centric Engineering, 2:e10, 2021. Sancarlos, A., Cameron, M., Le Peuvedic, J.-M., Groulier, J., Duval, J.-L., Cueto, E., & Chinesta, F. (2021). Learning stable reduced-order models for hybrid twins. Data-Centric Engineering. Data-Centric Engineering, 2:e10, 2021.
22.
Zurück zum Zitat Sancarlos, A., Cameron, M., Abel, A., Cueto, E., Duval, J.-L., & Chinesta, F. (2021). From ROM of electrochemistry to AI-based battery digital and hybrid twin. Archives of Computational Methods in Engineering, 28, 979–1015.MathSciNetCrossRef Sancarlos, A., Cameron, M., Abel, A., Cueto, E., Duval, J.-L., & Chinesta, F. (2021). From ROM of electrochemistry to AI-based battery digital and hybrid twin. Archives of Computational Methods in Engineering, 28, 979–1015.MathSciNetCrossRef
Metadaten
Titel
Hybrid Twin: An Intimate Alliance of Knowledge and Data
verfasst von
Francisco Chinesta
Fouad El Khaldi
Elias Cueto
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-031-21343-4_11