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Tensor representation of non-linear models using cross approximations

Author
Aguado, J.V.; Borzacchiello, D.; Kollepara, K. S.; Chinesta Soria, Francisco; Huerta, A.
Type of activity
Journal article
Journal
Journal of scientific computing
Date of publication
2019-02-19
DOI
10.1007/s10915-019-00917-2
Repository
http://hdl.handle.net/2117/165506 Open in new window
URL
https://link.springer.com/article/10.1007%2Fs10915-019-00917-2 Open in new window
Abstract
Tensor representations allow compact storage and efficient manipulation of multi-dimensional data. Based on these, tensor methods build low-rank subspaces for the solution of multi-dimensional and multi-parametric models. However, tensor methods cannot always be implemented efficiently, specially when dealing with non-linear models. In this paper, we discuss the importance of achieving a tensor representation of the model itself for the efficiency of tensor-based algorithms. We investigate the a...
Citation
Aguado, J.V. [et al.]. Tensor representation of non-linear models using cross approximations. "Journal of scientific computing", 19 Febrer 2019.
Keywords
Cross approximations, Low-rank tensor approximation, Multi-dimensional problems, Non-linear modeling, Parametrized PDE, Proper generalized decomposition, Reduced order modeling
Group of research
LACÀN - Numerical Methods for Applied Sciences and Engineering

Participants

  • Aguado, José Vicente  (author)
  • Borzacchiello, Domenico  (author)
  • Kollepara, Kiran Sagar  (author)
  • Chinesta Soria, Francisco  (author)
  • Huerta, Antonio  (author)