Methods of Chaos Detection and Predictability: Theory and Applications

One of the fundamental tasks in studies of dynamical systems is the development of accurate and efficient numerical tools for distinguishing between order and chaos, both locally and globally. The presence of chaos introduces limitations in our ability to accurately predict the evolution of a dynamical system at scales of different size, and is often associated with a loss in predictability. In many practical applications it is of great importance to determine the significance of this effect to the overall dynamics of the system.
There exists a plethora of methods for chaos detection and predictability. This interdisciplinary workshop will bring together specialists who have developed such methods as well as researchers applying those methods to answer pressing questions in the natural sciences. Its main aim is to comprehensively review the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as report recent applications of these techniques to different scientific fields like astronomy, mathematical physics, accelerator physics, geophysics, fluid dynamics, applied mathematics, and numerical analysis, with data coming from computer simulations,