Includes Index

Includes bibliography (p. 427-454) and references

1 First Encounter with Chaos
2 The Language of Dynamical Systems
3 Examples of Chaotic Behaviors
4 Probabilistic Approach to Chaos
5 Characterization of Chaotic Dynamical Systems
6 From Order to Chaos in Dissipative Systems
7 Chaos in Hamiltonian Systems
8 Chaos and Information Theory
9 Coarse-Grained Information and Large Scale Predictability
10 Chaos in Numerical and Laboratory Experiments
11 Chaos in Low Dimensional Systems
12 Spatiotemporal Chaos
13 Turbulence as a Dynamical System Problem
14 Chaos and Statistical Mechanics: Fermi-Pasta-Ulam a Case Study

Chaos: from simple models to complex systems aims to guide science and engineering students through chaos and nonlinear dynamics from classical examples to the most recent fields of research. The first part, intended for undergraduate and graduate students, is a gentle and self-contained introduction to the concepts and main tools for the characterization of deterministic chaotic systems, with emphasis to statistical approaches. The second part can be used as a reference by researchers as it focuses on more advanced topics including the characterization of chaos with tools of information theory and applications encompassing fluid and celestial mechanics, chemistry and biology. The book is novel in devoting attention to a few topics often overlooked in introductory textbooks and which are usually found only in advanced surveys such as: information and algorithmic complexity theory applied to chaos and generalization of Lyapunov exponents to account for spatiotemporal and non-infinitesimal perturbations.