Optimal finite-differences discretization for the diffusion equation from the perspective of large-deviation theory

IF 2.2 3区 物理与天体物理 Q2 MECHANICS
Naftali R Smith
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引用次数: 0

Abstract

When applying the finite-differences method to numerically solve the one-dimensional diffusion equation, one must choose discretization steps Δx, Δt in space and time, respectively. By applying large-deviation theory on the discretized dynamics, we analyze the numerical errors due to the discretization, and find that the (relative) errors are especially large in regions of space where the concentration of particles is very small. We find that the choice Δt=Δx2/(6D) , where D is the diffusion coefficient, gives optimal accuracy compared to any other choice (including, in particular, the limit Δt0 ), thus reproducing the known result that may be obtained using truncation error analysis. In addition, we give quantitative estimates for the dynamical lengthscale that describes the size of the spatial region in which the numerical solution is accurate, and study its dependence on the discretization parameters. We then turn to study the advection–diffusion equation, and obtain explicit expressions for the optimal Δt and other parameters of the finite-differences scheme, in terms of Δx, D and the advection velocity. We apply these results to study large deviations of the area swept by a diffusing particle in one dimension, trapped by an external potential |x| . We extend our analysis to higher dimensions by combining our results from the one dimensional case with the locally one-dimension method.
从大偏差理论的角度看扩散方程的最优有限差分离散法
应用有限差分法数值求解一维扩散方程时,必须在空间和时间上分别选择离散步长Δx、Δt。通过对离散动力学应用大偏差理论,我们分析了离散化带来的数值误差,发现在粒子浓度非常小的空间区域,(相对)误差尤其大。我们发现,选择 Δt=Δx2/(6D),其中 D 是扩散系数,与任何其他选择(尤其包括极限 Δt→0)相比,都能获得最佳精度,从而再现了使用截断误差分析可能获得的已知结果。此外,我们还给出了描述数值解精确的空间区域大小的动态长度尺度的定量估计,并研究了其与离散化参数的依赖关系。然后,我们转而研究平流扩散方程,并根据 Δx、D 和平流速度,得到有限差分方案的最优 Δt 和其他参数的明确表达式。我们将这些结果用于研究扩散粒子在一维中被外部势能 ∼|x| 困住后所扫过区域的大偏差。通过将一维情况下的结果与局部一维方法相结合,我们将分析扩展到更高维度。
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来源期刊
CiteScore
4.50
自引率
12.50%
发文量
210
审稿时长
1.0 months
期刊介绍: JSTAT is targeted to a broad community interested in different aspects of statistical physics, which are roughly defined by the fields represented in the conferences called ''Statistical Physics''. Submissions from experimentalists working on all the topics which have some ''connection to statistical physics are also strongly encouraged. The journal covers different topics which correspond to the following keyword sections. 1. Quantum statistical physics, condensed matter, integrable systems Scientific Directors: Eduardo Fradkin and Giuseppe Mussardo 2. Classical statistical mechanics, equilibrium and non-equilibrium Scientific Directors: David Mukamel, Matteo Marsili and Giuseppe Mussardo 3. Disordered systems, classical and quantum Scientific Directors: Eduardo Fradkin and Riccardo Zecchina 4. Interdisciplinary statistical mechanics Scientific Directors: Matteo Marsili and Riccardo Zecchina 5. Biological modelling and information Scientific Directors: Matteo Marsili, William Bialek and Riccardo Zecchina
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