Forecasting confined spatiotemporal chaos with genetic algorithms

Abstract

A technique to forecast spatiotemporal time series is presented. it uses a Proper Ortogonal or Karhunen-Loève Decomposition to encode large spatiotemporal data sets in a few time-series, and Genetic Algorithms to efficiently extract dynamical rules from the data. The method works very well for confined systems displaying spatiotemporal chaos, as exemplified here by forecasting the evolution of the onedimensional complex Ginzburg-Landau equation in a finite domain.

4 pages, 5 figures

References in corpus (3)

• Karhunen-Lo`eve Decomposition of Extensive Chaos
• Extensive Scaling and Nonuniformity of the Karhunen-Loève Decomposition for the Spiral-Defect Chaos State
• Average Patterns of Spatiotemporal Chaos: A Boundary Effect

Cited by in corpus (3)

• Parameter estimation in spatially extended systems: The Karhunen-Loeve and Galerkin multiple shooting approach
• Low-dimensional dynamical system model for observed coherent structures in ocean satellite data
• Optimal Neural Network Feature Selection for Spatial-Temporal Forecasting