Long range predictability of high dimensional chaotic dynamics

This thesis concerns the long range prediction of high dimensional chaotic systems. To this end, I investigate the important relationship between predictability and non-uniformity of information loss throughout the state space of a chaotic system. I introduce a genetic algorithm to build predictive models by exploiting this nonuniformity. The algorithm searches for the regions of state space which remain most predictable for a given time into the future. I use the algorithm to investigate the predictabilty of both model chaotic systems and physical data from a fluid flow experiment.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Year dc:date

2011

Author dc:creator

• Meyer, Thomas Patrick

Contributors dc:contributor

• Packard, Norman H.

• high dimensional chaotic dynamics
• long range prediction
• chaotic systems
• model chaotic systems

Statement dc:rights

• 1992 Thomas Patrick Meyer

Language dc:language

en

Identifier

3488342

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/18908