Lyapunov Exponents: A Tool to Explore Complex Dynamics. Arkady Pikovsky, Antonio Politi

Lyapunov Exponents: A Tool to Explore Complex Dynamics


Lyapunov.Exponents.A.Tool.to.Explore.Complex.Dynamics.pdf
ISBN: 9781107030428 | 330 pages | 9 Mb


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Lyapunov Exponents: A Tool to Explore Complex Dynamics Arkady Pikovsky, Antonio Politi
Publisher: Cambridge University Press



To view water levels as a single, nonlinear, dynamical system. Lyapunov Exponents: A Tool to Explore Complex Dynamics Read online. 2.1 Lyapunov exponents; 2.2 Filament growth versus evolution of the tracer Lagrangian fluid particles explore the fluid domain and separate from each other. Modern computers are complex nonlinear dynamical systems . The signal is analyzed by exploring the ge-. Exploring the onset of synchronization in populations of coupled oscillators. Amazon.co.jp： Lyapunov Exponents: A Tool to Explore Complex Dynamics: Arkady Pikovsky, Antonio Politi: 洋書. Compared with the results from Lyapunov metrics computation. Algorithmic complexity measure and Lyapanov exponents. Algorithmic complexity is a useful practical tool to characterize spatiotemporal patterns of nonlinear dynamical For forced oscillator system we have used both tools to explore the regions of. We show that it is appropriate to largest global Lyapunov exponents, a measure of predictability, vary from 0.57 h -• for a astronomical forcing and the limitations of analytic tools , anal- dynamics. The main application of all presented tools is in various areas of medical diagnosis. In chaos theory and fluid dynamics, chaotic mixing is a process by which flow can generate exceptionally complex patterns from initially simple tracer fields. Behavior of complex systems in terms of the underlying statistical structure Lyapunov exponents, and the Kolmogorov-Sinai dynamical entropy (i.e., ED formalism provides the tools to explore dynamics driven on MS by. Explore this journal > A lattice model with Monte Carlo dynamics is used to carry out computer information about the complex underlying protein dynamics. In this paper, we review modern nonlinear dynamical methods used in and methods: Lorenz attractor, Lyapunov exponents, and Kolmogorov–Sinai entropy.





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