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Title: Nonlinear intrinsic variables and state reconstruction in multiscale simulations

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.
Authors:
 [1] ; ;  [2] ;  [3] ;  [1] ;  [4]
  1. Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544 (United States)
  2. Department of Mathematics, Yale University, New Haven, Connecticut 06520 (United States)
  3. Department of Exact Sciences, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv (Israel)
  4. (United States)
Publication Date:
OSTI Identifier:
22251461
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Chemical Physics; Journal Volume: 139; Journal Issue: 18; Other Information: (c) 2013 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALANINES; COMPUTERIZED SIMULATION; ENZYMES; MOLECULAR DYNAMICS METHOD; MONTE CARLO METHOD; STOCHASTIC PROCESSES