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Advanced model order reduction and artificial intelligence techniques empowering advanced structural mechanics simulations: application to crash test analyses

Abstract : This paper proposes a general framework for expressing parametrically quantities of interest related to the solution of complex structural mechanics models, in particular the ones involved in crash analyses where strongly coupled nonlinear and dynamic behaviors coexist with space-time localized mechanisms. Advanced nonlinear regressions able to proceed in the low-data limit, enabling to accommodate heterogeneous parameters, will be proposed and their performances evaluated in the case of crash simulations. As soon as these parametric expressions will be determined, they can be used for generating large amounts of realizations of the quantity of interest for different choices of the parameters, for supporting data-analytics. On the other hand, such parametric representations allow the use advanced optimization techniques, evaluate sensitivities and propagate uncertainty all them under the stringent real-time constraint.
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https://hal-cnam.archives-ouvertes.fr/hal-03711027
Contributor : Aurélie Puybonnieux Connect in order to contact the contributor
Submitted on : Friday, July 1, 2022 - 10:14:20 AM
Last modification on : Friday, August 5, 2022 - 3:33:42 PM

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Distributed under a Creative Commons Attribution 4.0 International License

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Victor Limousin, Xavier Delgerie, Emmanuel Leroy, Rubén Ibáñez, Clara Argerich, et al.. Advanced model order reduction and artificial intelligence techniques empowering advanced structural mechanics simulations: application to crash test analyses. Mechanics & Industry, EDP Sciences, 2019, 20 (8), pp.804. ⟨10.1051/meca/2020009⟩. ⟨hal-03711027⟩

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