A data-fitting procedure for chaotic time series

摘要

In this paper we introduce data characterizations for fitting chaotic data to linear combinations of one-dimensional maps (say, of the unit interval) for use in subgrid-scale turbulence models. We test the efficacy of these characterizations on data generated by a chaotically-forced Burgers' equation and demonstrate very satisfactory results in terms of modeled time series, power spectra and delay maps.