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International Conference on Complex Systems (ICCS2006)

Making forecasts for chaotic physical processes

Christopher Danforth
Department of Mathematics and Statistics, College of Enginee

James Yorke
Institute of Physical Science and Technology, University of Maryland

     Full text: Not available
     Last modified: April 25, 2006

Making a prediction for a chaotic physical process involves specifying the probability associated with each possible outcome. Ensembles of solutions are frequently used to estimate this probability distribution. However, for a typical complex system H and model L of that system, no solution of L remains close to H for all time. We propose an alternative. In this talk, we show how to ``inflate" or systematically perturb the ensemble of solutions of L so that some ensemble member remains close to H for orders of magnitude longer than unperturbed solutions of L. This is true even when the perturbations are significantly smaller than the model error.

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