Adaptive deployment of model reductions for tau-leaping simulation

被引:7
|
作者
Wu, Sheng [1 ]
Fu, Jin [1 ]
Petzold, Linda R. [1 ]
机构
[1] Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA
来源
JOURNAL OF CHEMICAL PHYSICS | 2015年 / 142卷 / 20期
基金
美国国家科学基金会;
关键词
ACCELERATED STOCHASTIC SIMULATION; STEADY-STATE ASSUMPTION;
D O I
10.1063/1.4921638
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Multiple time scales in cellular chemical reaction systems often render the tau-leaping algorithm inefficient. Various model reductions have been proposed to accelerate tau-leaping simulations. However, these are often identified and deployed manually, requiring expert knowledge. This is time-consuming and prone to error. In previous work, we proposed a methodology for automatic identification and validation of model reduction opportunities for tau-leaping simulation. Here, we show how the model reductions can be automatically and adaptively deployed during the time course of a simulation. For multiscale systems, this can result in substantial speedups. (C) 2015 AIP Publishing LLC.
引用
收藏
页数:9
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