IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5608286.html
   My bibliography  Save this article

A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition

Author

Listed:
  • Rubén Ibáñez
  • Emmanuelle Abisset-Chavanne
  • Amine Ammar
  • David González
  • Elías Cueto
  • Antonio Huerta
  • Jean Louis Duval
  • Francisco Chinesta

Abstract

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 settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.

Suggested Citation

  • Rubén Ibáñez & Emmanuelle Abisset-Chavanne & Amine Ammar & David González & Elías Cueto & Antonio Huerta & Jean Louis Duval & Francisco Chinesta, 2018. "A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition," Complexity, Hindawi, vol. 2018, pages 1-11, November.
    • Handle: RePEc:hin:complx:5608286
    DOI: 10.1155/2018/5608286
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/5608286.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/5608286.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/5608286?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jordan Mann & J. Nathan Kutz, 2016. "Dynamic mode decomposition for financial trading strategies," Quantitative Finance, Taylor & Francis Journals, vol. 16(11), pages 1643-1655, November.
    2. González, D. & Masson, F. & Poulhaon, F. & Leygue, A. & Cueto, E. & Chinesta, F., 2012. "Proper Generalized Decomposition based dynamic data driven inverse identification," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(9), pages 1677-1695.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Avalos, Edgar & Datta, Amitava & Rosato, Anthony D. & Blackmore, Denis & Sen, Surajit, 2020. "Dynamics in a confined mass–spring chain with 1∕r repulsive potential: Strongly nonlinear regime," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    2. Berger, Julien & Mendes, Nathan, 2017. "An innovative method for the design of high energy performance building envelopes," Applied Energy, Elsevier, vol. 190(C), pages 266-277.
    3. Gyurhan Nedzhibov, 2024. "Delay-Embedding Spatio-Temporal Dynamic Mode Decomposition," Mathematics, MDPI, vol. 12(5), pages 1-18, March.
    4. Elmore, Clay T. & Dowling, Alexander W., 2021. "Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition," Energy, Elsevier, vol. 232(C).
    5. Jinxiang Xi & Weizhong Zhao, 2019. "Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-22, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:5608286. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.