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2023 | OriginalPaper | Chapter

Hybrid Twin: An Intimate Alliance of Knowledge and Data

Authors : Francisco Chinesta, Fouad El Khaldi, Elias Cueto

Published in: The Digital Twin

Publisher: 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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previous chapter Data and Data Management in the Context of Digital Twins

next chapter Artificial Intelligence and the Digital Twin: An Essential Combination

Chinesta, F., Keunings, R., & Leygue, A. (2014). The proper generalized decomposition for advanced numerical simulations. A primer (Springerbriefs). Springer. https://www.springer.com/gp/book/9783319028644CrossRefMATH

Chinesta, F., Huerta, A., Rozza, G., & Willcox, K. (2015). Chapitre dans l’Encyclopedia of Computational Mechanics. In E. Stein, R. de Borst, & T. Hughes (Eds.), Model order reduction (2nd ed.). Wiley. https://doi.org/10.1002/9781119176817.ecm2110CrossRef

Borzacchiello, D., Aguado, J. V., & Chinesta, F. (2019). Non-intrusive sparse subspace learning for parametrized problems. Archives of Computational Methods in Engineering, 26(2), 303–326. https://doi.org/10.1007/s11831-017-9241-4MathSciNetCrossRef

Chinesta, F., Leygue, A., Bordeu, F., Aguado, J. V., Cueto, E., Gonzalez, D., Alfaro, I., Ammar, A., & Huerta, A. (2013). Parametric PGD based computational vademecum for efficient design, optimization and control. Archives of Computational Methods in Engineering, 20(1), 31–59. 10.1007/s11831-013-9080-x.MathSciNetCrossRefMATH

Chinesta, F., Cueto, E., Abisset, E., Duval, J. L., & El Khaldi, F. (2020). Virtual, Digital and Hybrid Twins. A new paradigm in data-based engineering and engineered data. Archives of Computational Methods in Engineering, 27(1), 105–134. https://doi.org/10.1007/s11831-018-9301-4MathSciNetCrossRef

Lopez, E., Gonzalez, D., Aguado, J., Abisset-Chavanne, E., Cueto, E., Binetruy, C., & Chinesta, F. (2018). A manifold learning approach for integrated computational materials engineering. Archives of Computational Methods in Engineering, 25(1), 59–68. https://doi.org/10.1007/s11831-016-9172-5MathSciNetCrossRefMATH

González, D., Chinesta, F., & Cueto, E. (2019). Thermodynamically consistent data-driven computational mechanics. Continuum Mechanics and Thermodynamics, 31, 239–253. https://doi.org/10.1007/s00161-018-0677-zMathSciNetCrossRef

Ibanez, R., Abisset-Chavanne, E., Aguado, J. V., Gonzalez, D., Cueto, E., Chinesta, F. (2018). A manifold-based methodological approach to data-driven computational elasticity and inelasticity. Archives of Computational Methods in Engineering, 25(1), 47–57. https://doi.org/10.1007/s11831-016-9197-9

Quaranta, G., Duval, J. L., Lopez, E., Abisset-Chavanne, E., Huerta, A., & Chinesta, F. (2019). Structural health monitoring by combining machine learning and dimensionality reduction techniques. Revista Internacional de Metodos Numericos, 35(1). https://www.scipedia.com/public/Quaranta_et_al_2018a

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Ibanez, 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, Article ID 5608286. https://doi.org/10.1155/2018/5608286

11.

Moya, B., Gonzalez, D., Alfaro, I., Chinesta, F., & Cueto, E. (2019). Learning slosh dynamics by means of data. Computational Mechanics, 64, 511–523. https://doi.org/10.1007/s00466-019-01705-3MathSciNetCrossRefMATH

12.

Argerich Martín, C., Ibáñez Pinillo, R., Barasinski, A., & Chinesta, F. (2019). Code2Vect: An efficient heterogenous data classifier and nonlinear regression technique. CRAS Mécanique, 347(11), 754–761. https://doi.org/10.1016/j.crme.2019.11.002CrossRef

13.

Montáns, F. J., Chinesta, F., Gómez-Bombarelli, R., & Kutz, J. N. (2019). Data-driven modeling and learning in science and engineering. CRAS Mécanique, 347(11), 845–855. https://doi.org/10.1016/j.crme.2019.11.009CrossRef

14.

Reille, A., Hascoet, N., Ghnatios, C., Ammar, A., Cueto, E., Duval, J. L., Chinesta, F., & Keunings, R. (2019). Incremental Dynamic Mode Decomposition: A reduced-model learner operating at the low-data limit. CRAS Mécanique, 347(11), 780–792. https://doi.org/10.1016/j.crme.2019.11.003CrossRef

15.

Gonzalez, D., Chinesta, F., Cueto, E. (2019). Learning corrections for hyperelastic models from data. Frontiers in Materials - Section Computational Materials Science, 6(14).

16.

Ibanez, R., Abisset-Chavanne, E., Gonzalez, D., Duval, J. L., Cueto, E., Chinesta, F. (2019). Hybrid constitutive modeling: Data-driven learning of corrections to plasticity models. International Journal of Material Forming, 12, 717–725.

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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.

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.

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.

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.

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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.

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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

Title: Hybrid Twin: An Intimate Alliance of Knowledge and Data
Authors: Francisco Chinesta
Fouad El Khaldi
Elias Cueto
Publisher: Springer International Publishing
Book: The Digital Twin
Print ISBN: 978-3-031-21342-7

Electronic ISBN: 978-3-031-21343-4

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-21343-4_11

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