Nonlinear intrinsic variables and state reconstruction in multiscale simulations

Carmeline J. Dsilva, Ronen Talmon, Neta Rabin, Ronald R. Coifman, Ioannis G. Kevrekidis

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discuss and illustrate the use of nonlinear intrinsic variables (NIV) in the mining of high-dimensional multiscale simulation data. In particular, we focus on the way NIV allows us to functionally merge different simulation ensembles, and different partial observations of these ensembles, as well as to infer variables not explicitly measured. The approach relies on certain simple features of the underlying process variability to filter out measurement noise and systematically recover a unique reference coordinate frame. We illustrate the approach through two distinct sets of atomistic simulations: a stochastic simulation of an enzyme reaction network exhibiting both fast and slow time scales, and a molecular dynamics simulation of alanine dipeptide in explicit water.

Original languageEnglish (US)
Article number184109
JournalJournal of Chemical Physics
Volume139
Issue number18
DOIs
StatePublished - Nov 14 2013

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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