A data-fitting procedure for chaotic time series

In this paper the authors 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. They 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.

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Citation "\"McDonough, J.M.; Mukerji, S. [Univ. of Kentucky, Lexington, KY (United States). Dept. of Mechanical Engineering]; Chung, S. [Univ. of Illinois, Urbana, IL (United States)]\""
Is NETL associated "\"Yes\""
NETL Point of Contact "\"Roy Long\""
NETL Point of Contact's Email "\"Roy.long@netl.doe.gov\""
NETL program or project "\"KMD\""
Publication Date "\"10/1/1998\""
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  • AmeriGEOSS
  • Global Provider
Tags
  • amerigeo
  • amerigeoss
  • ckan
  • edx
  • energy
  • energy-data-exchange
  • geo
  • geoss
  • global
  • kmd
isopen True
license_id other-open
license_title Other (Open)
metadata_created 2025-11-25T23:08:23.842917
metadata_modified 2025-11-25T23:08:23.842922
notes In this paper the authors 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. They 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.
num_resources 1
num_tags 10
title A data-fitting procedure for chaotic time series