Regular Dynamics: Newton to Poincaré; KAM Theorem | Bifurcations: Routes to Chaos, Stability and Instability | Reconstruction of Phase Space: Regular and Chaotic Motions; Observed Chaos | Choosing Time Delays: Chaos as an Information Source; Average Mutual Information. | Choosing the Dimension of Reconstructed Phase Space | Invariants of the Motion: Global & Local Lyapunov Exponents; Lorenz Model | Modeling Chaos: Local & Global Models; Phase Space Models | Signal Separation: Probabilistic Cleaning; ’Blind’ Signal Separation | Control and Chaos: Parametric Control; Examples of Control (including magnetoelastic ribbon, electric circuits, cardiac tissue) | Synchronization of Chaotic Systems: Identical or Dissimilar Systems; Chaotic Nonlinear Circuits | Other Example Systems: Laser Intensity Fluctuations; Volume Fluctuations of the Great Salt Lake; Motion in a Fluid Boundary Layer | Estimating in Chaos: Cramér-Rao Bounds | The Chaos Toolkit: Making ’Physics’ out of Chaos
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This text examines methods for separating the signal of physical interest from the contamination by chaotic noise, for investigating the phase space of the chaotic noise and its properties, and for modelling the behaviour of the chaotic noise. The author considers the means of controlling chaotic behaviour and using this control to communicate between source and receiver. The emphasis throughout is on the use of the modern mathematical tools for investigating chaotic behaviour to uncover properties of physical systems.
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