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arXiv 0808.3863math.NAcs.NA
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多尺度随机化学动力学的并行时间模拟

Parallel in Time Simulation of Multiscale Stochastic Chemical Kinetics

Uppsala University(乌普萨拉大学)

Stefan Engblom

AI总结:

提出并分析了一种时间并行算法parareal,用于化学动力学中的随机模型,通过宏观尺度快速预测与介观尺度随机模拟的结合,实现了非刚性和刚性问题的快速收敛,并建立了均质化解的收敛性。

摘要:

分析并应用了时间并行算法parareal的一个版本于化学动力学中的随机模型。宏观尺度上的快速预测器(串行评估)以通常的反应速率方程形式可用。使用随机模拟算法在介观尺度上(并行)获得过程的精确实现。底层随机描述是由泊松测度驱动的跳跃过程。在这个具有挑战性的设定中,我们建立了一个收敛结果,表明解的均质化是有利的。我们设计了一种简单但高度通用的技术。三个代表计算系统生物学领域的数值实验说明了该方法。对于非刚性问题,即使存在随机效应,该方法也能快速收敛。对于刚性问题,我们反而能够快速收敛到均质化解。总体而言,该方法在宏观确定性尺度和介观随机尺度之间构建了一座有吸引力的桥梁。这种构造显然也可以应用于其他领域的随机模型。

英文摘要:

A version of the time-parallel algorithm parareal is analyzed and applied to stochastic models in chemical kinetics. A fast predictor at the macroscopic scale (evaluated in serial) is available in the form of the usual reaction rate equations. A stochastic simulation algorithm is used to obtain an exact realization of the process at the mesoscopic scale (in parallel). The underlying stochastic description is a jump process driven by the Poisson measure. A convergence result in this arguably difficult setting is established suggesting that a homogenization of the solution is advantageous. We devise a simple but highly general such technique. Three numerical experiments on models representative to the field of computational systems biology illustrate the method. For non-stiff problems, it is shown that the method is able to quickly converge even when stochastic effects are present. For stiff problems we are instead able to obtain fast convergence to a homogenized solution. Overall, the method builds an attractive bridge between on the one hand, macroscopic deterministic scales and, on the other hand, mesoscopic stochastic ones. This construction is clearly possible to apply also to stochastic models within other fields.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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