Wong-Zakai approximation of solutions to reflecting stochastic differential equations on domains in Euclidean spaces
Shigeki Aida, Kosuke Sasaki

TL;DR
This paper investigates the Wong-Zakai approximation for solutions to reflecting stochastic differential equations in Euclidean domains, establishing convergence results under broad conditions.
Contribution
It provides the first rigorous proof of $L^p$ convergence for Wong-Zakai approximations in reflecting SDEs on general domains.
Findings
Proves $L^p$ convergence of Wong-Zakai approximations
Establishes convergence in the space of continuous functions
Applies to a wide class of Euclidean domains
Abstract
In this paper, we study the Wong-Zakai approximation of the solution to the stochastic differential equation on a domain in a Euclidean space with normal reflection at the boundary. We prove the convergence of the approximation in under some general conditions on .
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
TopicsAdvanced Mathematical Modeling in Engineering · Stochastic processes and financial applications · Nonlinear Differential Equations Analysis
