BIOMS Center for Modeling and Simulation in the Biosciences, D-69120 Heidelberg, Germany.
J Chem Phys. 2012 Aug 14;137(6):064114. doi: 10.1063/1.4742909.
We propose a kinetic Monte Carlo method for the simulation of subdiffusive random walks on a cartesian lattice. The random walkers are subject to viscoelastic forces which we compute from their individual trajectories via the fractional Langevin equation. At every step the walkers move by one lattice unit, which makes them differ essentially from continuous time random walks, where the subdiffusive behavior is induced by random waiting. To enable computationally inexpensive simulations with n-step memories, we use an approximation of the memory and the memory kernel functions with a complexity O(log n). Eventual discretization and approximation artifacts are compensated with numerical adjustments of the memory kernel functions. We verify with a number of analyses that this new method provides binary fractional random walks that are fully consistent with the theory of fractional brownian motion.
我们提出了一种用于模拟笛卡尔格子上亚扩散随机行走的动力学蒙特卡罗方法。随机行走者受到粘弹性力的作用,我们通过分数朗之万方程从它们的个体轨迹计算这些力。在每一步中,行走者移动一个晶格单位,这使得它们与连续时间随机行走有本质上的区别,在连续时间随机行走中,亚扩散行为是由随机等待引起的。为了能够进行具有 n 步记忆的计算成本低廉的模拟,我们使用具有复杂度 O(log n)的记忆和记忆核函数的近似值。最终的离散化和近似伪影通过对记忆核函数进行数值调整来补偿。我们通过多项分析验证了这种新方法提供的二进制分数随机游走与分数布朗运动理论完全一致。