A convergent stochastic scalar auxiliary variable method
Stefan Metzger

TL;DR
This paper introduces an extended scalar auxiliary variable method for stochastic PDEs, enabling unconditionally stable, higher-order, linear schemes with proven convergence and practical numerical demonstrations.
Contribution
It extends the scalar auxiliary variable approach to stochastic PDEs by incorporating higher order terms, resulting in a stable, convergent, and practical numerical scheme.
Findings
The scheme is unconditionally energy stable.
Convergence to strong solutions is rigorously proven.
Numerical simulations confirm the scheme's effectiveness.
Abstract
We discuss an extension of the scalar auxiliary variable approach, which was originally introduced by Shen et al. ([Shen, Xu, Yang, J. Comput. Phys., 2018]) for the discretization of deterministic gradient flows. By introducing an additional scalar auxiliary variable, this approach allows to derive a linear scheme, while still maintaining unconditional stability. Our extension augments the approximation of the evolution of this scalar auxiliary variable with higher order terms, which enables its application to stochastic partial differential equations. Using the stochastic Allen--Cahn equation as a prototype for nonlinear stochastic partial differential equations with multiplicative noise, we propose an unconditionally energy stable, linear, fully discrete finite element scheme based on our augmented scalar auxiliary variable method. Recovering a discrete version of the energy estimate…
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
TopicsStochastic processes and financial applications · Solidification and crystal growth phenomena · Advanced Mathematical Modeling in Engineering
