A Regularized Auxiliary Variable (RAV) Approach for Gradient Flows

TL;DR

This paper introduces a regularized auxiliary variable (RAV) method for gradient flows, providing unconditionally stable, accurate, and robust time-discrete schemes with optimal error estimates and numerical validation.

Contribution

The paper develops a novel RAV scheme that ensures energy stability and accuracy for gradient flows, improving upon existing auxiliary variable methods.

Findings

RAV scheme achieves unconditional energy stability.

The method provides optimal error estimates in $L^ Infty(0,T;H^2)$.

Numerical results validate the accuracy and effectiveness of RAV.

Abstract

In this paper, we propose a regularized auxiliary variable (RAV) approach and construct accurate and robust time-discrete schemes for a large class of gradient flows. By introducing an auxiliary variable $r=0$ and constructing an auxiliary equation that naturally fits into the energy relation, the numerical solution $r^{n+1}$ of the auxiliary variable is corrected at each time step to preserve consistency with the original system. The developed RAV scheme satisfies unconditional energy stability with respect to the original variables, and in certain cases the original energy law can be directly recovered. Furthermore, we obtain a uniform bound on the norm of the numerical solution, which allows us to establish the optimal error estimate in $L^\infty(0,T;H^2)$ for the second-order scheme without any restriction on the time step. We present ample numerical results, including comparisons with the scalar auxiliary variable (SAV) approach, to demonstrate the accuracy and effectiveness of the proposed RAV approach.