Non-Markovian optimal prediction

Alexandre J. Chorin*, Ole H. Hald, Raz Kupferman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Optimal prediction methods compensate for a lack of resolution in the numerical so lution of complex problems through the use of prior statistical Information. We know from previous work that in the presence of strong underresolution a good approximation needs a non-Markovian memory, determined by an equation for the orthogonal, i.e., unresolved, dynamics. We present a simple approximation of the orthogonal dynamics, which involves an ansatz and a Monte-Carlo evaluation of autocorrelations. The analysis provides a new understanding of the fluctuation-dissipation formulas of statistical physics. An example is given.

Original languageAmerican English
Pages (from-to)99-109
Number of pages11
JournalMonte Carlo Methods and Applications
Issue number1-2
StatePublished - Jan 2001


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