Mechanics & Industry
Volume 20, Number 8, 2019
Selected scientific topics in recent applied engineering – 20 Years of the ‘French Association of Mechanics – AFM’
|Number of page(s)||16|
|Published online||25 February 2020|
- F. Chinesta, A. Huerta, G. Rozza, K. Willcox, Model Order Reduction. Chapter in the Encyclopedia of Computational Mechanics, Second Edition, Erwin Stein, René de Borst & Tom Hughes Edt., John Wiley & Sons Ltd. (2015) [Google Scholar]
- F. Chinesta, P. Ladeveze, E. Cueto, A short review in model order reduction based on proper generalized decomposition. Arch. Comput. Methods Eng. 18, 395–404 (2011) [Google Scholar]
- F. Chinesta, A. Leygue, F. Bordeu, J.V. Aguado, E. Cueto, D. Gonzalez, I. Alfaro, A. Ammar, A. Huerta, PGD-based computational vademecum for efficient design, optimization and control, Arch. Comput. Methods Eng. 20, 31–59 (2013) [Google Scholar]
- F. Chinesta, R. Keunings, A. Leygue, The Proper Generalized Decomposition for Advanced Numerical Simulations. A primer. Springerbriefs, Springer (2014) [Google Scholar]
- D. Borzacchiello, J.V. Aguado, F. Chinesta, Non-intrusive sparse subspace learning for parametrized problems. Arch. Comput. Methods Eng. 26, 303–326 (2019) [Google Scholar]
- R. Ibanez, E. Abisset-Chavanne, A. Ammar, D. Gonzalez, E. Cueto, A. Huerta, J.L. Duval, F. Chinesta, A multi-dimensional data-driven sparse identification technique: the sparse proper generalized decomposition, Complexity 2018, 5608286 (2018) [Google Scholar]
- Safety Wissen, February 2019, European News Car Assessment Programme (EuroNCAP), available at https://www.safetywissen.com/#/requirement/ [Google Scholar]
- P.J. Schmid, Dynamic mode decomposition of numerical and experimental data, J. Fluid Mech. 656, 5–28 (2010) [Google Scholar]
- S.L. Brunton, J.L. Proctor, N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, PNAS, April 12, 113, 3932–3937 (2016) [Google Scholar]
- C. Argerich, R. Ibanez, F. Chinesta, Code2vect: An efficient heterogenous data classifier and nonlinear regression technique. CRAS Mécanique. 347, 754–761 (2019) [Google Scholar]
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