FN Archimer Export Format
PT J
TI Extending the extended dynamic mode decomposition with latent observables: the latent EDMD framework
BT 
AF Ouala, Said
   Chapron, Bertrand
   Collard, Fabrice
   Gaultier, Lucile
   Fablet, Ronan
AS 1:1;2:2;3:3;4:3;5:1;
FF 1:;2:PDG-ODE-LOPS-SIAM;3:;4:;5:;
C1 IMT Atlantique; Lab-STICC, 29200 Brest, France
   Ifremer, LOPS, 29200 Brest, France
   ODL, 29200 Brest, France
C2 IMT ATLANTIQUE, FRANCE
   IFREMER, FRANCE
   OCEANDATALAB, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   DOAJ
   copubli-france
IF 6.8
TC 0
UR https://archimer.ifremer.fr/doc/00835/94709/102191.pdf
LA English
DT Article
DE ;dynamical systems;Koopman operator;extended dynamic mode decomposition;Kalman filter
AB Bernard O Koopman proposed an alternative view of dynamical systems based on linear operator theory, in which the time evolution of a dynamical system is analogous to the linear propagation of an infinite-dimensional vector of observables. In the last few years, several works have shown that finite-dimensional approximations of this operator can be extremely useful for several applications, such as prediction, control, and data assimilation. In particular, a Koopman representation of a dynamical system with a finite number of dimensions will avoid all the problems caused by nonlinearity in classical state-space models. In this work, the identification of finite-dimensional approximations of the Koopman operator and its associated observables is expressed through the inversion of an unknown augmented linear dynamical system. The proposed framework can be regarded as an extended dynamical mode decomposition that uses a collection of latent observables. The use of a latent dictionary applies to a large class of dynamical regimes, and it provides new means for deriving appropriate finite-dimensional linear approximations to high-dimensional nonlinear systems. 
PY 2023
PD JUL
SO Machine Learning-science And Technology
SN 2632-2153
PU IOP Publishing
VL 4
IS 2
UT 000980296700001
DI 10.1088/2632-2153/acccd6
ID 94709
ER

EF