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

2022/10/31 by Martin Hutzenthaler, Hutzenthaler, Martin, Robert Link +1
Decision Sciences · Economics, Econometrics and Finance · #60H15 (Primary) 65C30 (Secondary) #FOS: Mathematics #Financial Risk and Volatility Modeling #Probability (math.PR) #Probability and Risk Models #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2210.17536

openalex publication_date 2022/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• Strong convergence rates on the whole probability space for space-time discrete numerical approximation schemes for stochastic Burgers equations
• Weak error estimates of Galerkin approximations for the stochastic Burgers equation driven by additive trace-class noise
• Strong convergence of a fully discrete scheme for stochastic Burgers equation with fractional-type noise
• Strong convergence of an explicit full-discrete scheme for stochastic Burgers-Huxley equation
• Global well-posedness and regularity of 3D Burgers equation with multiplicative noise