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 th...

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Bibliographic Details
Published in:arXiv.org 2013-07
Main Authors: Dsilva, Carmeline J, Talmon, Ronen, Rabin, Neta, Coifman, Ronald R, Kevrekidis, Ioannis G
Format: Article
Language:English
Subjects:
Alanine
Chemical reactions
Computer simulation
Mathematical models
Molecular dynamics
Noise measurement
Organic chemistry
Simulation
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1307.7580