2010
DOI: 10.1016/j.physd.2010.03.009
|View full text |Cite
|
Sign up to set email alerts
|

Coherent sets for nonautonomous dynamical systems

Abstract: We describe a mathematical formalism and numerical algorithms for identifying and tracking slowly mixing objects in nonautonomous dynamical systems. In the autonomous setting, such objects are variously known as almost-invariant sets, metastable sets, persistent patterns, or strange eigenmodes, and have proved to be important in a variety of applications. In this current work, we explain how to extend existing autonomous approaches to the nonautonomous setting. We call the new time-dependent slowly mixing obje… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

3
138
0

Year Published

2011
2011
2023
2023

Publication Types

Select...
6
2
1

Relationship

2
7

Authors

Journals

citations
Cited by 110 publications
(141 citation statements)
references
References 45 publications
3
138
0
Order By: Relevance
“…However, the derivation will be presented in detail in the interests of coherence, since several adjustments are needed; such as the performance of the calculation at a movable pointx u (p) rather than a fixed location, the emphasis on locating the manifold rather than distances between manifolds, the lack of necessity of a homoclinic connection, the legitimacy of discarding higher-order terms, and the potential divergence of the Melnikov expression. This last issue is also related to the possible divergence of a boundary term in going from (20) to (21) in Holmes [32]. Most geometric Melnikov developments following formal perturbative analysis [26,4,32] discard the O(ε) terms in the Melnikov integral, which actually needs additional consideration since an integration over a noncompact interval needs to be performed.…”
Section: Proof Of Theorem 21 (Unstable Manifold's Normal Displacemenmentioning
confidence: 99%
See 1 more Smart Citation
“…However, the derivation will be presented in detail in the interests of coherence, since several adjustments are needed; such as the performance of the calculation at a movable pointx u (p) rather than a fixed location, the emphasis on locating the manifold rather than distances between manifolds, the lack of necessity of a homoclinic connection, the legitimacy of discarding higher-order terms, and the potential divergence of the Melnikov expression. This last issue is also related to the possible divergence of a boundary term in going from (20) to (21) in Holmes [32]. Most geometric Melnikov developments following formal perturbative analysis [26,4,32] discard the O(ε) terms in the Melnikov integral, which actually needs additional consideration since an integration over a noncompact interval needs to be performed.…”
Section: Proof Of Theorem 21 (Unstable Manifold's Normal Displacemenmentioning
confidence: 99%
“…These form time-varying flow separators and specifically are the boundaries of Lagrangian coherent structures [30,47,28,29,46,52,20,23]. Their determination is therefore crucial to understanding fluid transport in oceanic dynamics but remains difficult due to the fact that theoretical tools in the genuinely timeaperiodic setting are lacking.…”
mentioning
confidence: 99%
“…64 Hence, the connection between set-oriented statistical methods and topological methods in dynamical systems made in this paper provides an additional tool for analyzing There is work on spectral analysis of the "mixing matrix" 65 that is closely related to the identification of almost invariant sets and ACSs, and connections have been made between this mixing matrix analysis and the "strange eigenmode" that arises from spectral analysis of the continuous advection-diffusion operator. 66,67 The topological information available from analyzing trajectories of ACS suggests that a similar approach can be applied to the braiding of eigenvectors in these related methods.…”
Section: Discussionmentioning
confidence: 99%
“…is obtained from the tensor transformation rule for the Eulerian homogeneous, isotropic diffusion tensor I 3×3 which is implicitly contained in (11). In this Lagrangian frame of reference, the original advection diffusion equation (11) becomes a diffusion equation, albeit with an inhomogeneous, anisotropic and time-dependent diffusion tensor field D. The data-driven detection methods used in this paper can be interpreted as discretization approaches to this Lagrangian diffusion equation (12).…”
Section: A Lagrangian Passive Scalar Transport Perspectivementioning
confidence: 99%