期刊
JOURNAL OF CHEMICAL PHYSICS
卷 139, 期 18, 页码 -出版社
AIP Publishing
DOI: 10.1063/1.4828457
关键词
-
资金
- US Department of Energy Computational Science Graduate Fellowship [DE-FG02-97ER25308]
- US Department of Energy [DE-FG02-10ER26024, DE-FG02-09ER25877]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1309858] Funding Source: National Science Foundation
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. (C) 2013 AIP Publishing LLC.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据