pith. sign in

arxiv: 2210.17536 · v1 · pith:DE4AR6RVnew · submitted 2022-10-31 · 🧮 math.PR

Strong convergence rates for full-discrete approximations of stochastic Burgers equations with multiplicative noise

classification 🧮 math.PR
keywords approximationsburgersconvergenceequationsestablishfull-discretemultiplicativenoise
0
0 comments X
read the original abstract

In this article we establish strong convergence rates on the whole probability space for explicit full-discrete approximations of stochastic Burgers equations with multiplicative trace-class noise. The key step in our proof is to establish uniform exponential moment estimates for the numerical approximations.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Strong convergence of a fully discrete scheme for stochastic Burgers equation with fractional-type noise

    math.NA 2024-08 unverdicted novelty 5.0

    A spectral Galerkin plus nonlinear-tamed accelerated exponential Euler scheme is proved to converge strongly for the stochastic Burgers equation with cylindrical fractional Brownian motion noise where H is in (1/2, 1).

  2. Strong convergence of an explicit full-discrete scheme for stochastic Burgers-Huxley equation

    math.NA 2024-08 unverdicted novelty 5.0

    Proves strong convergence rates for a spectral Galerkin plus nonlinear-tamed exponential integrator scheme on the stochastic Burgers-Huxley equation.