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首页> 外文期刊>The Journal of Chemical Physics >Accurate implementation of leaping in space: The spatial partitionedleaping algorithm
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Accurate implementation of leaping in space: The spatial partitionedleaping algorithm

机译:精确实现空间跳跃:空间分区学习算法

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There is a great need for accurate and efficient computational approaches that can account for both the discrete and stochastic nature of chemical interactions as well as spatial inhomogeneities and diffusion. This is particularly true in biology and nanoscale materials science, where the common assumptions of deterministic dynamics and well-mixed reaction volumes often break down. In this article, we present a spatial version of the partitioned-leaping algorithm, a multiscale accelerated-stochastic simulation approach built upon the τ-leaping framework of Gillespie. We pay special attention to the details of the implementation, particularly as it pertains to the time step calculation procedure. We point out conceptual errors that have been made in this regard in prior implementations of spatial τ-leaping and illustrate the manifestation of these errors through practical examples. Finally, we discuss the fundamental difficulties associated with incorporating efficient exact-stochastic techniques, such as the next-subvolume method, into a spatial leaping framework and suggest possible solutions.
机译:迫切需要能够解决化学相互作用的离散性和随机性以及空间不均匀性和扩散的准确有效的计算方法。在生物学和纳米级材料科学中尤其如此,确定性动力学和充分混合的反应体积的通常假设经常会被打破。在本文中,我们介绍了分区学习算法的空间版本,这是一种基于Gillespie的τ学习框架的多尺度加速随机仿真方法。我们特别注意实现的细节,尤其是与时间步长计算过程有关的细节。我们指出了在空间τ浸出的现有实现中在这方面已经出现的概念性错误,并通过实际示例说明了这些错误的体现。最后,我们讨论了将有效的精确随机技术(例如下一个子体积方法)纳入空间跳跃框架所涉及的基本困难,并提出了可能的解决方案。

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