Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

Multidimensional Approximation of Nonlinear Dynamical Systems

  • A key task in the field of modeling and analyzing nonlinear dynamical systems is the recovery of unknown governing equations from measurement data only. There is a wide range of application areas for this important instance of system identification, ranging from industrial engineering and acoustic signal processing to stock market models. In order to find appropriate representations of underlying dynamical systems, various data-driven methods have been proposed by different communities. However, if the given data sets are high-dimensional, then these methods typically suffer from the curse of dimensionality. To significantly reduce the computational costs and storage consumption, we propose the method multidimensional approximation of nonlinear dynamical systems (MANDy) which combines data-driven methods with tensor network decompositions. The efficiency of the introduced approach will be illustrated with the aid of several high-dimensional nonlinear dynamical systems.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Author:Patrick GelßORCiD, Stefan Klus, Jens Eisert, Christof Schütte
Document Type:Article
Parent Title (English):Journal of Computational and Nonlinear Dynamics
Year of first publication:2019
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.