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.