Dimension Reduction for Systems with Slow Relaxation

In Memory of Leo P. Kadanoff

Shankar C Venkataramani, Raman C. Venkataramani, Juan M. Restrepo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We 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.

Original languageEnglish (US)
Pages (from-to)1-42
Number of pages42
JournalJournal of Statistical Physics
DOIs
StateAccepted/In press - Mar 18 2017

Fingerprint

Model Reduction
Dimension Reduction
Dissipative Dynamical System
Oil Spill
Memory Term
Reduced Model
First-principles
Evaporation
Stochastic Model
Linear Model
High-dimensional
Filter
Modeling
dynamical systems
oils
evaporation
filters
Framework

Keywords

  • Aging
  • Dimension reduction
  • Glassy systems
  • Mori–Zwanzig projection
  • Multi-scale
  • Oil spills
  • Sloppy models
  • Slow relaxation
  • Weathering

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

Dimension Reduction for Systems with Slow Relaxation : In Memory of Leo P. Kadanoff. / Venkataramani, Shankar C; Venkataramani, Raman C.; Restrepo, Juan M.

In: Journal of Statistical Physics, 18.03.2017, p. 1-42.

Research output: Contribution to journalArticle

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