A Non-compact Positivity-Preserving Numerical Scheme for Elliptic Differential Equations Based on Mathematical Expectation
This work addresses a domain-specific problem in computational mathematics for anisotropic diffusion equations, where classical schemes often fail, but it is incremental as it builds on existing probabilistic methods.
The authors tackled the problem of solving linear non-divergence form elliptic equations, particularly for anisotropic diffusion with mixed derivatives, by proposing a non-compact, positivity-preserving numerical scheme based on the Feynman-Kac formula, achieving convergence rates such as O(h) for Dirichlet boundaries and O(h^{1/2}) for Neumann boundaries in numerical experiments.
We propose a novel non-compact, positivity-preserving scheme for linear non-divergence form elliptic equations. Based on the Feynman--Kac formula, the solution is represented as a conditional expectation associated with a diffusion process.Instead of using compact Markov chain approximations, we construct a wide-stencil scheme by approximating the expectation with carefully designed transition probabilities, ensuring both consistency and positivity preservation. The method is effective for anisotropic diffusion problems with mixed derivatives, where classical schemes typically fail unless the covariance matrix is diagonally dominant. A key feature of the proposed framework is its robust treatment of boundary conditions. For Dirichlet boundaries, we introduce a quadtree-based non-uniform stopping-time strategy, achieving $O(h)$ accuracy. For Neumann boundaries, a discrete specular reflection mechanism is employed, yielding $O(h^{1/2})$ convergence. Periodic boundaries are handled through modular wrapping, also achieving $O(h)$ accuracy. The resulting schemes are unconditionally stable and positivity-preserving due to their probabilistic structure. Numerical experiments confirm the theoretical convergence rates under all boundary conditions considered.