Results 211 to 220 of about 472,452 (242)

Exploring Modeling Techniques for Soft Arms: A Survey on Numerical, Analytical, and Data-Driven Approaches. [PDF]

Biomimetics (Basel)
Liu S, Yu H, Ding N, He X, Liu H, Zhang J.   +5 more
europepmc   +1 more source

Conditional POD for predicting extreme events in turbulent flow time signals. [PDF]

Sci Rep
Martín D, Grau J, Jofre L.
europepmc   +1 more source

Electroencephalography-driven brain-network models for personalized interpretation and prediction of neural oscillations.

Clin Neurophysiol
Dubcek T, Ledergerber D, Thomann J, Aiello G, Serra Garcia M, Imbach L, Polania R.   +6 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Koopman Operator Family Spectrum for Nonautonomous Systems

SIAM Journal on Applied Dynamical Systems, 2018
For any non-autonomous dynamical system, the family of Koopman operators, as well as related Koopman eigenvalues and eigenfunctions, are parameterized by a time pair. Therefore, a logical approach in the data-driven algorithms for the non-autonomous Koopman mode decomposition is the application of a DMD method on the moving stencils of snapshots in ...
Nélida Črnjarić, Igor Mezic, Senka Macesic   +2 more
exaly   +3 more sources

Applied Koopman operator theory for power systems technology [PDF]

Nonlinear Theory and Its Applications IEICE, 2016
Koopman operator is a composition operator defined for a dynamical system described by nonlinear differential or difference equation. Although the original system is nonlinear and evolves on a finite-dimensional state space, the Koopman operator itself ...
Takashi Hikihara, Igor Mezic, Fredrik Raak   +2 more
exaly   +2 more sources

The Koopman Operator in Systems and Control

, 2020
This book provides a broad overview of state-of-the-art research at the intersection of the Koopman operator theory and control theory. It also reviews novel theoretical results obtained and efficient numerical methods developed within the framework of ...
core   +3 more sources

Sparsity Structures for Koopman and Perron--Frobenius Operators

SIAM Journal on Applied Dynamical Systems, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Schlosser, Corbinian, Korda, Milan
openaire   +3 more sources

Koopman operator learning using invertible neural networks [PDF]

Journal of Computational Physics
In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an infinite but linear system using a set of observable functions.
Yue Qiu, Jianguo Huang
exaly   +2 more sources

Decomposition theorems for koopman operators

Nonlinear Analysis: Theory, Methods & Applications, 1997
Let \(L^1(X)= L^1_\mu(X)= L^1(X,\Sigma,\mu)\), \(L^\infty(X)= L^\infty_\mu(X)= L^\infty(X,\Sigma,\mu)\) denote the usual Lebesgue spaces on a \(\sigma\)-finite measure space \((X,\Sigma,\mu)\), and let \(S:X\to X\) be a non-singular map, i.e. \(\mu(A)=0\) implies \(\mu(S^{-1}(A))=0\), \(A\in\Sigma\). Denote \(\nu=\mu\circ S^{-1}\), \(\varphi= d\nu/d\mu\
openaire   +1 more source