Entropy of spatiotemporal data as a dynamic truncation indicator for model reduction applications

Leonidas G. Bleris, Mayuresh V. Kothare

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We have provided a methodology [1] for retrieving spatial and temporal eigenfunctions from an ensemble of data. Focusing on a Newtonian fluid flow problem, we illustrate that the efficacy of these two families of eigenfunctions can be different when used in model reduction projections. The above observation can be of critical importance for low-order modeling of distributed parameter systems in on-line control applications, due to the computational savings that are introduced. For the particular fluid flow problem, we introduce the use of the entropy of the data ensemble as the criterion for choosing the appropriate eigenfunction family. Finally, we examine the use of the entropy as a dynamic truncation indicator.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Number of pages6
StatePublished - 2005
Externally publishedYes
Event2005 American Control Conference, ACC - Portland, OR, United States
Duration: Jun 8 2005Jun 10 2005


Other2005 American Control Conference, ACC
Country/TerritoryUnited States
CityPortland, OR

ASJC Scopus subject areas

  • Control and Systems Engineering


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