AffiliationUniv Arizona, Dept Math
MetadataShow full item record
CitationVenkataramani, S.C., Venkataramani, R.C. & Restrepo, J.M. Dimension Reduction for Systems with Slow Relaxation. J Stat Phys 167, 892–933 (2017). https://doi.org/10.1007/s10955-017-1761-7
JournalJOURNAL OF STATISTICAL PHYSICS
Rights© Springer Science+Business Media New York 2017
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractWe develop reduced, stochastic models for high dimensional, dissipative dynamical systems that relax very slowly to equilibrium and can encode long term memory. We present a variety of empirical and first principles approaches for model reduction, and build a mathematical framework for analyzing the reduced models. We introduce the notions of universal and asymptotic filters to characterize ‘optimal’ model reductions for sloppy linear models. We illustrate our methods by applying them to the practically important problem of modeling evaporation in oil spills.
Note12 month embargo; published online 18 March 2017
VersionFinal accepted manuscript
SponsorsDivision of Mathematical Sciences