Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
by
Mezić, Igor
, Korda, Milan
in
Algorithms
/ Analysis
/ Approximation
/ Classical Mechanics
/ Convergence
/ Decomposition
/ Dynamical systems
/ Economic Theory/Quantitative Economics/Mathematical Methods
/ Eigenvalues
/ Eigenvectors
/ Forecasting
/ Mathematical and Computational Engineering
/ Mathematical and Computational Physics
/ Mathematics
/ Mathematics and Statistics
/ Numerical analysis
/ Sampling
/ Subspaces
/ Theoretical
/ Topology
2018
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
by
Mezić, Igor
, Korda, Milan
in
Algorithms
/ Analysis
/ Approximation
/ Classical Mechanics
/ Convergence
/ Decomposition
/ Dynamical systems
/ Economic Theory/Quantitative Economics/Mathematical Methods
/ Eigenvalues
/ Eigenvectors
/ Forecasting
/ Mathematical and Computational Engineering
/ Mathematical and Computational Physics
/ Mathematics
/ Mathematics and Statistics
/ Numerical analysis
/ Sampling
/ Subspaces
/ Theoretical
/ Topology
2018
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
by
Mezić, Igor
, Korda, Milan
in
Algorithms
/ Analysis
/ Approximation
/ Classical Mechanics
/ Convergence
/ Decomposition
/ Dynamical systems
/ Economic Theory/Quantitative Economics/Mathematical Methods
/ Eigenvalues
/ Eigenvectors
/ Forecasting
/ Mathematical and Computational Engineering
/ Mathematical and Computational Physics
/ Mathematics
/ Mathematics and Statistics
/ Numerical analysis
/ Sampling
/ Subspaces
/ Theoretical
/ Topology
2018
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
Journal Article
On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
2018
Request Book From Autostore
and Choose the Collection Method
Overview
Extended dynamic mode decomposition (EDMD) (Williams et al. in J Nonlinear Sci 25(6):1307–1346,
2015
) is an algorithm that approximates the action of the Koopman operator on an
N
-dimensional subspace of the space of observables by sampling at
M
points in the state space. Assuming that the samples are drawn either independently or ergodically from some measure
μ
, it was shown in Klus et al. (J Comput Dyn 3(1):51–79,
2016
) that, in the limit as
M
→
∞
, the EDMD operator
K
N
,
M
converges to
K
N
, where
K
N
is the
L
2
(
μ
)
-orthogonal projection of the action of the Koopman operator on the finite-dimensional subspace of observables. We show that, as
N
→
∞
, the operator
K
N
converges in the strong operator topology to the Koopman operator. This in particular implies convergence of the predictions of future values of a given observable over any finite time horizon, a fact important for practical applications such as forecasting, estimation and control. In addition, we show that accumulation points of the spectra of
K
N
correspond to the eigenvalues of the Koopman operator with the associated eigenfunctions converging weakly to an eigenfunction of the Koopman operator, provided that the weak limit of the eigenfunctions is nonzero. As a by-product, we propose an analytic version of the EDMD algorithm which, under some assumptions, allows one to construct
K
N
directly, without the use of sampling. Finally, under additional assumptions, we analyze convergence of
K
N
,
N
(i.e.,
M
=
N
), proving convergence, along a subsequence, to weak eigenfunctions (or eigendistributions) related to the eigenmeasures of the Perron–Frobenius operator. No assumptions on the observables belonging to a finite-dimensional invariant subspace of the Koopman operator are required throughout.
Publisher
Springer US,Springer Nature B.V
This website uses cookies to ensure you get the best experience on our website.