Predictability of Chaotic Dynamics

This insightful work explores the interplay between modeling and predictability in chaotic systems, particularly in the context of astronomy and numerical simulations.

This book delves into the computational aspects of predictability within dynamical systems, particularly focusing on scenarios where observations, modeling, and computation are deeply intertwined. Unlike controlled physical systems in laboratories, astronomical studies often lack the ability to modify key parameters of the objects being analyzed. Consequently, numerical simulations become a vital tool for understanding these systems, making the reliability of such simulations increasingly important. In this interdisciplinary context, physics provides the simulated models, nonlinear dynamics contributes insights into chaotic behavior, and computer science facilitates numerical implementation.

Predictability of Chaotic Dynamics introduces the connection between models and their predictability, emphasizing concepts from nonlinear dynamics. A key focus is on the sensitivity to initial conditions, utilizing Lyapunov exponents to quantify this sensitivity. The book illustrates these concepts through well-known continuous dynamical systems, including the Contopoulos, Hénon-Heiles, and Rössler systems. This second edition significantly expands upon the first, offering new perspectives on various predictability issues, including applications in machine decision-making, partial differential equations, and the analysis of attractors and basins.

Moreover, the sections dedicated to the application of these ideas in astronomy have been extensively enhanced. The book begins by outlining basic aspects of predictability in astronomy and then progresses to a comprehensive analysis of galactic potential, illustrating the relevance of chaotic dynamics in this field. Overall, Predictability of Chaotic Dynamics serves as a crucial resource for understanding the complexities of predictability in chaotic systems.