This work presents a probabilistic scheme for solving semilinear nonlocal diffusion equations with volume constraints and integrable kernels. The nonlocal model of interest is defined by a time-dependent semilinear partial integro-differential equation (PIDE), in which the integro-differential operator consists of both local convection-diffusion and nonlocal diffusion operators. Our numerical scheme is based on the direct approximation of the nonlinear Feynman-Kac formula that establishes a link between nonlinear PIDEs and stochastic differential equations. The exploitation of the Feynman-Kac representation successfully avoids solving dense linear systems arising from nonlocality operators. Compared with existing stochastic approaches, our method can achieve first-order convergence after balancing the temporal and spatial discretization errors, which is a significant improvement of existing probabilistic/stochastic methods for nonlocal diffusion problems. Error analysis of our numerical scheme is established. The effectiveness of our approach is shown in two numerical examples. The first example considers a three-dimensional nonlocal diffusion equation to numerically verify the error analysis results. The second example presents a physics problem motivated by the study of heat transport in magnetically confined fusion plasmas.
翻译:这项工作提出了一个解决具有体积限制和不可分离内核的半线性非局部扩散方程式的概率方案。 非本地利益模型由时间依赖的半线性半线性局部异化方程式(PIDE)定义,在这种方程式中,内地差异操作器既包括局部对流扩散操作器,也包括非局部扩散操作器。我们的数字方法以非线性Feynman-Kac公式的直接近似值为基础,该公式在非线性 PIDE 和随机差异方程式之间建立了联系。 Feynman-Kac 代表法的利用成功地避免了解决非本地操作器产生的密集线性系统。与现有的微分化方法相比,我们的方法可以在平衡时间和空间离散误差后达到一级趋同,这是对非本地扩散问题现有概率性/分析方法的重大改进。我们的数字方法的错误分析在两个数字示例中显示我们的方法的有效性。第一个例子是,从三维非局部扩散方程式到数字性磁性磁性磁性流分析的结果。