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

Abstract

When applying the finite-differences method to numerically solve the one-dimensional diffusion equation, one must choose discretization steps $\Delta x$, $\Delta t$ 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 $\Delta t={\Delta x}^2/(6D)$, where $D$ is the diffusion coefficient, gives optimal accuracy compared to any other choice (including, in particular, the limit $\Delta t\to 0$), 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 $\Delta t$ and other parameters of the finite-differences scheme, in terms of $\Delta x$, $D$ 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 $\sim |x|$. We extend our analysis to higher dimensions by combining our results from the one dimensional case with the locally one-dimension method.

12 pages, 5 figures

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Cited by in corpus (2)

• Anomalous scalings of fluctuations of the area swept by a Brownian particle trapped in a $|x|$ potential
• Subleading-order theory for condensation transitions in large deviations of sums of independent and identically distributed random variables