Hybrid Twin: An Intimate Alliance of Knowledge and Data
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-21343-4_11
Reference22 articles.
1. Springerbriefs;F Chinesta,2014
2. 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.ecm2110
3. 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/s11831-017-9241-4
4. 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.
5. 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/s11831-018-9301-4
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