Extended-PGD Model Reduction for Nonlinear Solid Mechanics Problems Involving Many Parameters

Abstract : Reduced models and especially those based on Proper Generalized Decomposition (PGD) are decision-making tools which are about to revolutionize many domains. Unfortunately, their calculation remains problematic for problems involving many parameters, for which one can invoke the “curse of dimensionality”. The paper starts with the state-of-the-art for nonlinear problems involving stochastic parameters. Then, an answer to the challenge of many parameters is given in solid mechanics with the so-called “parameter-multiscale PGD”, which is based on the Saint-Venant principle.
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https://hal.archives-ouvertes.fr/hal-01926425
Contributeur : Charles Paillet <>
Soumis le : lundi 19 novembre 2018 - 11:24:49
Dernière modification le : mercredi 21 novembre 2018 - 01:12:27

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Pierre Ladevèze, Charles Paillet, David Néron. Extended-PGD Model Reduction for Nonlinear Solid Mechanics Problems Involving Many Parameters. Advances in Computational Plasticity, pp.201-220, 2017, 〈10.1007/978-3-319-60885-3_10〉. 〈hal-01926425〉

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