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Journal Articles Comptes Rendus Mécanique Year : 2019

Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit

Abstract

The present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then they can be expressed very efficiently using few terms of a tensor decomposition. An efficient procedure is proposed as well as a way for extending it to nonlinear settings while keeping limited the impact of data noise. The proposed methodology is then validated by considering a nonlinear elastic problem and constructing the model relating tractions and displacements at the observation points.
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Dates and versions

hal-02561899 , version 1 (04-05-2020)

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Agathe Reille, Nicolas Hascoët, Chady Ghnatios, Amine Ammar, Elías G. Cueto, et al.. Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit. Comptes Rendus Mécanique, 2019, 347 (11), pp.780-792. ⟨10.1016/j.crme.2019.11.003⟩. ⟨hal-02561899⟩
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