Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit
Comptes Rendus Mécanique(2019)
摘要
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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关键词
Machine learning,Advanced regression,Tensor formats,PGD,Mode decomposition,Nonlinear reduced modeling
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