Estimating dimension of inertial manifold from unstable periodic orbits
arXiv:1604.01859 · doi:10.1103/PhysRevLett.117.024101
Abstract
We provide numerical evidence that a finite-dimensional inertial manifold on which the dynamics of a chaotic dissipative dynamical system lives can be constructed solely from the knowledge of a set of unstable periodic orbits. In particular, we determine the dimension of the inertial manifold for Kuramoto-Sivashinsky system, and find it to be equal to the `physical dimension' computed previously via the hyperbolicity properties of covariant Lyapunov vectors.
arXiv comment6 pages, 3 pdf figures, uses revtex4
References in corpus (12)
- Characterizing dynamics with covariant Lyapunov vectors
- Visualizing the geometry of state space in plane Couette flow
- Hopf's last hope: spatiotemporal chaos in terms of unstable recurrent patterns
- On the state space geometry of the Kuramoto-Sivashinsky flow in a periodic domain
- Hyperbolicity and the effective dimension of spatially-extended dissipative systems
- Covariant Lyapunov vectors
- Unstable recurrent patterns in Kuramoto-Sivashinsky dynamics
- Reduction of SO(2) symmetry for spatially extended dynamical systems
- Hyperbolic decoupling of tangent space and effective dimension of dissipative systems
- Symmetry reduction in high dimensions, illustrated in a turbulent pipe
- Geometry of inertial manifolds probed via a Lyapunov projection method
- What does dynamical systems theory teach us about fluids?
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