Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit
TypeArticles dans des revues avec comité de lecture
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.
Showing items related by title, author, creator and subject.
GHNATIOS, Chady; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CUETO, Elías; DUVAL, Jean Louis; CHINESTA, Francisco (Elsevier B.V., 2019)This work aims at proposing a new procedure for parametric problems whose separated representation has been considered difficult, or whose SVD compression impacted the results in terms of performance and accuracy. The ...
Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction IBAÑEZ, R.; ABISSET-CHAVANNE, Emmanuelle; CUETO, Elías G.; AMMAR, Amine; DUVAL, Jean Louis; CHINESTA, Francisco (Springer, 2019)Compressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to ...
IBÁÑEZ PINILLO, Rubén; AMMAR, Amine; CUETO, Elías G.; HUERTA, Antonio; DUVAL, Jean Louis; CHINESTA, Francisco (Wiley, 2019)Solutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The ...
A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition IBÁÑEZ, Rubén; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; GONZALEZ, David; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean Louis; CHINESTA, Francisco (Hindawi Limited, 2018)Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional ...
SCHEUER, Adrien; AMMAR, Amine; ABISSET-CHAVANNE, Emmanuelle; CUETO, Elías; CHINESTA, Francisco; KEUNINGS, Roland; ADVANI, Suresh G. (Tech Science Press, 2018)Describing the orientation state of the particles is often critical in fibre suspension applications. Macroscopic descriptors, the so-called second-order orientation tensor (or moment) leading the way, are often preferred ...