3.3
Extracting low-order stochastic models from data
Daan Crommelin, New York Univ., New York, NY; and E. Vanden-Eijnden
We present a numerical technique to derive optimal stochastic models (Markov chains or SDEs) for the description of the evolution of a few interesting variables in large-sized dynamical systems. The technique is based on constructing the stochastic model whose eigenfunctions and eigenvalues are the closest, in some appropriate norm, to the ones gathered from (possibly non-Markovian) timeseries. We apply the technique by extracting a stochastic model for the temporal evolution of the first few principal components taken from an atmosphere model dataset.
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Session 3, Stochastic Modeling
Monday, 13 June 2005, 1:45 PM-2:45 PM, Ballroom B
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