Linearization in the large of nonlinear systems and Koopman operator spectrum
By Yueheng Lan, Igor Mezi'c. 2013
203 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/8402da10e93748d056cc49e61ba0d9b172f38663
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Steven L. Brunton
- J. Nathan Kutz
- Joshua L. Proctor
- Bingni W. Brunton
- Igor Mezi'c
- Eurika Kaiser
- Thomas Duriez
- Bernd R. Noack
In Computer Networks and Communications
- Steven L. Brunton
- J. Nathan Kutz
- Bernd R. Noack
- Thomas Duriez
- Eurika Kaiser
- Bingni W. Brunton
- Joshua L. Proctor
- Jeff Moehlis
In Statistical and Nonlinear Physics
- Steven L. Brunton
- J. Nathan Kutz
- Joshua L. Proctor
- Bingni W. Brunton
- Eurika Kaiser
- Igor Mezi'c
- Maizar Raissi
- Bernd R. Noack
Citations in the corpus
- Isostables, isochrons, and Koopman spectrum for the action-angle representation of stable fixed point dynamics (2013, background): “For higherdimensional systems (or two-dimensional systems with a sink node), global linearization involves several Koopman eigenfunctions sj(x) (see [20] for a detailed study), which can be obtained through the generalized Laplace averages (see Remark 3).”
- Chaos as an intermittently forced linear system (2016, methodology): “For a detailed discussion on the Koopman operator, there are many excellent research articles [70, 82, 16, 17, 61] and reviews [18, 71].”
- Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, methodology): “The dynamic mode decomposition (DMD) [237, 228, 269] is a promising new technique for system identification that has strong connections to nonlinear dynamical systems through Koopman spectral analysis [162, 163, 187, 47, 188, 170, 41].”