# Iterative and Non-iterative Splitting approach of a stochastic Burgers' equation

## TL;DR

This paper introduces iterative and non-iterative splitting methods for solving stochastic Burgers' equations, demonstrating that iterative schemes are more accurate and efficient due to the equation's nonlinearity.

## Contribution

The paper develops and compares iterative and non-iterative splitting methods, highlighting the improved accuracy and efficiency of iterative approaches for stochastic Burgers' equations.

## Key findings

- Iterative splitting methods outperform non-iterative methods in accuracy.
- Non-iterative methods are based on Lie-Trotter and Strang-splitting.
- Iterative schemes leverage exponential integrators for better performance.

## Abstract

In this paper we present iterative and noniterative splitting methods, which are used to solve stochastic Burgers' equations. The non-iterative splitting methods are based on Lie-Trotter and Strang-splitting methods, while the iterative splitting approaches are based on the exponential integrators for stochastic differential equations. Based on the nonlinearity of the Burgers' equation, we have investigated that the iterative schemes are more accurate and efficient as the non-iterative methods.