A Non-compact Positivity-Preserving Scheme for Parabolic PDE via Conditional Expectation
Haoran Xu, Jie Ren, Xingye Yue

TL;DR
This paper introduces a new non-compact, positivity-preserving numerical scheme for linear parabolic PDEs based on the Feynman-Kac formula, effectively handling anisotropic diffusion and boundary conditions with improved accuracy and stability.
Contribution
It develops a wide stencil, non-compact scheme that preserves positivity and handles complex boundary conditions with high accuracy, unlike classical compact schemes.
Findings
Scheme is unconditionally stable and positive preserving.
Achieves O(Δt^{1/2}) and O(Δt) accuracy for boundary treatments.
Numerical experiments confirm theoretical convergence rates.
Abstract
We propose a novel non-compact, positivity-preserving scheme for linear non-divergence form parabolic equations. Based on the Feynman-Kac formula, the solution is expressed as a conditional expectation of an associated diffusion process. Instead of using compact Markov chain approximations, we employ a wide stencil scheme to approximate the conditional expectation, ensuring consistency and positivity preservation. This method is effective for anisotropic diffusion with mixed derivatives, where classical schemes often fail unless the covariance matrix is diagonally dominated. A key feature of our framework is its robust treatment of boundary conditions, which avoids the accuracy loss commonly encountered in BZ and semi-Lagrangian schemes. For Dirichlet boundaries, we introduce (i) a quad-tree non-uniform stopping time scheme with O() accuracy and (ii) a quad-tree…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical methods for differential equations · Mathematical Approximation and Integration · Markov Chains and Monte Carlo Methods
